From 05f7fafe7f23b1bd02b59d0abfeccf068a9862fc Mon Sep 17 00:00:00 2001
From: anandhkb <aanandh@nvidia.com>
Date: Sun, 30 Nov 2025 12:58:04 -0800
Subject: [PATCH 1/2] Add Capacitated Facility Location Problem (CFLP)
 optimization example

- Create cflp_cuopt_milp/ directory with structured contents
- Add cflp_cuopt_milp.ipynb notebook with:
  - MILP formulation for DC placement and customer assignment
  - GPU detection and cuOpt installation instructions
  - Comprehensive visualizations (network design, cost breakdown, utilization)
  - Semi-relaxation extension with fractional assignments
  - Multi-period capacity expansion extension with time discounting
  - Business insights and performance summary
- Add README.md with problem overview, installation, and usage instructions
- Follow same structure as car_rental_optimization example
---
 cflp_cuopt_milp/README.md             |  107 ++
 cflp_cuopt_milp/cflp_cuopt_milp.ipynb | 2299 +++++++++++++++++++++++++
 2 files changed, 2406 insertions(+)
 create mode 100644 cflp_cuopt_milp/README.md
 create mode 100644 cflp_cuopt_milp/cflp_cuopt_milp.ipynb

diff --git a/cflp_cuopt_milp/README.md b/cflp_cuopt_milp/README.md
new file mode 100644
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+# Capacitated Facility Location Optimization
+
+A Mixed Integer Linear Program (MILP) example for optimizing distribution center (DC) placement and customer assignment to minimize total logistics cost.
+
+## Problem Overview
+
+A logistics company needs to determine:
+- Which distribution centers (DCs) to open from a set of candidate locations
+- How to assign customers to open DCs
+- How to balance fixed operating costs with transportation costs
+
+**Goal**: Minimize total annualized logistics cost while meeting demand and respecting capacity constraints
+
+## Model Assumptions
+
+**Important**: This model uses the following assumptions:
+
+1. **Single assignment**: Each customer is assigned to exactly one DC
+2. **Capacity limits**: Each DC has a maximum pallet-handling capacity
+3. **Fixed costs**: Opening a DC incurs a fixed annual operating cost
+4. **Known demand**: Customer demand is deterministic and known
+5. **Euclidean distances**: Transportation costs are proportional to straight-line distance
+
+This formulation works well for:
+- Distribution network design
+- Warehouse location planning
+- Supply chain optimization
+- Retail store placement
+
+**Note**: Extensions include multi-period capacity expansion and fractional (LP relaxed) assignments.
+
+## Notebook Contents
+
+### Setup & Data
+- 5 candidate distribution centers
+- 20 customers with varying demand
+- Synthetic 2D coordinates for visualization
+- Transportation cost proportional to distance ($0.05/pallet-km)
+- Fixed DC operating costs (~$80,000-$120,000)
+
+### Optimization
+- **Decision Variables**: 
+  - DC open/close binaries (y_i)
+  - Customer assignment binaries (x_ij)
+- **Objective**: Minimize fixed costs + transportation costs
+- **Constraints**: 
+  - Assignment: Each customer assigned to exactly one DC
+  - Capacity: DC load cannot exceed capacity
+  - Linking: Customers can only be assigned to open DCs
+
+### Results & Analysis
+- Optimal DC selection and network design
+- Customer-to-DC assignment matrix
+- DC utilization rates
+- Cost breakdown (fixed vs. transportation)
+- Network visualization with assignment arcs
+
+### Extensions
+- **LP Relaxation**: Fractional assignments for lower bound analysis
+- **Multi-Period Expansion**: Time-phased DC opening decisions with discounting
+
+## Installation
+
+**Requirements**: 
+- NVIDIA GPU with CUDA 12 or 13 support
+- Python 3.9+
+
+**Install cuOpt** (choose one based on your CUDA version):
+
+```bash
+# For CUDA 12
+pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu12
+
+# For CUDA 13  
+pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu13
+```
+
+**Install visualization dependencies**:
+
+```bash
+pip install matplotlib seaborn
+```
+
+## Quick Start
+
+```bash
+jupyter notebook cflp_cuopt_milp.ipynb
+```
+
+The notebook includes GPU detection and will guide you through any missing dependencies.
+
+## Possible Extensions
+
+**Multi-Period Planning** (included in notebook):
+- Open DCs over multiple time periods
+- Time-discounted costs
+- Time-varying demand with growth rates
+- Capacity expansion decisions
+
+**Additional**:
+- Multiple DC capacity tiers
+- Product-specific assignment
+- Stochastic demand scenarios
+- Service level constraints (max distance)
+- Multi-echelon supply chain
+- Inventory considerations
+
diff --git a/cflp_cuopt_milp/cflp_cuopt_milp.ipynb b/cflp_cuopt_milp/cflp_cuopt_milp.ipynb
new file mode 100644
index 0000000..84e6f69
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+++ b/cflp_cuopt_milp/cflp_cuopt_milp.ipynb
@@ -0,0 +1,2299 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Capacitated Facility Location with NVIDIA cuOpt (MILP)\n",
+        "\n",
+        "This notebook formulates and solves a capacitated facility location problem (CFLP) using the NVIDIA cuOpt MILP Python API.\n",
+        "\n",
+        "## Problem Description\n",
+        "\n",
+        "A logistics company operates a distribution network. They need to determine:\n",
+        "- Which distribution centers (DCs) to open from a set of candidate locations\n",
+        "- How to assign customers to open DCs to fulfill their demand\n",
+        "\n",
+        "The goal is to minimize total annual logistics cost while:\n",
+        "- Meeting all customer demand\n",
+        "- Respecting DC capacity constraints\n",
+        "- Balancing fixed operating costs with transportation costs\n",
+        "\n",
+        "### Model Assumptions\n",
+        "\n",
+        "**Important**: This model makes the following simplifying assumptions:\n",
+        "1. **Single assignment**: Each customer is assigned to exactly one DC\n",
+        "2. **Capacity limits**: Each DC has a maximum pallet-handling capacity per week\n",
+        "3. **Fixed costs**: Opening a DC incurs a fixed annual operating cost\n",
+        "4. **Known demand**: Customer demand is deterministic and known in advance\n",
+        "5. **Euclidean distances**: Transportation costs are proportional to straight-line distance\n",
+        "\n",
+        "This formulation is well-suited for:\n",
+        "- Distribution network design\n",
+        "- Warehouse location planning\n",
+        "- Supply chain optimization\n",
+        "- Retail store placement decisions\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Environment Setup\n",
+        "\n",
+        "First, let's check if we have a GPU available and install necessary dependencies.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
+              "            <h3>✅ GPU is enabled</h3>\n",
+              "            <pre>| NVIDIA-SMI 535.161.08             Driver Version: 535.161.08   CUDA Version: 12.2     |</pre>\n",
+              "        </div>\n",
+              "        "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 1,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import subprocess\n",
+        "import html\n",
+        "from IPython.display import display, HTML\n",
+        "\n",
+        "def check_gpu():\n",
+        "    try:\n",
+        "        result = subprocess.run([\"nvidia-smi\"], capture_output=True, text=True, timeout=5)\n",
+        "        result.check_returncode()\n",
+        "        lines = result.stdout.splitlines()\n",
+        "        gpu_info = lines[2] if len(lines) > 2 else \"GPU detected\"\n",
+        "        gpu_info_escaped = html.escape(gpu_info)\n",
+        "        display(HTML(f\"\"\"\n",
+        "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
+        "            <h3>✅ GPU is enabled</h3>\n",
+        "            <pre>{gpu_info_escaped}</pre>\n",
+        "        </div>\n",
+        "        \"\"\"))\n",
+        "        return True\n",
+        "    except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError, IndexError) as e:\n",
+        "        display(HTML(\"\"\"\n",
+        "        <div style=\"border:2px solid red;padding:15px;border-radius:10px;background:#ffeeee;\">\n",
+        "            <h3>⚠️ GPU not detected!</h3>\n",
+        "            <p>This notebook requires a <b>GPU runtime</b>.</p>\n",
+        "            \n",
+        "            <h4>If running in Google Colab:</h4>\n",
+        "            <ol>\n",
+        "              <li>Click on <b>Runtime → Change runtime type</b></li>\n",
+        "              <li>Set <b>Hardware accelerator</b> to <b>GPU</b></li>\n",
+        "              <li>Then click <b>Save</b> and <b>Runtime → Restart runtime</b>.</li>\n",
+        "            </ol>\n",
+        "            \n",
+        "            <h4>If running in Docker:</h4>\n",
+        "            <ol>\n",
+        "              <li>Ensure you have <b>NVIDIA Docker runtime</b> installed (<code>nvidia-docker2</code>)</li>\n",
+        "              <li>Run container with GPU support: <code>docker run --gpus all ...</code></li>\n",
+        "              <li>Or use: <code>docker run --runtime=nvidia ...</code> for older Docker versions</li>\n",
+        "              <li>Verify GPU access: <code>docker run --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi</code></li>\n",
+        "            </ol>\n",
+        "            \n",
+        "            <p><b>Additional resources:</b></p>\n",
+        "            <ul>\n",
+        "              <li><a href=\"https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\" target=\"_blank\">NVIDIA Container Toolkit Installation Guide</a></li>\n",
+        "            </ul>\n",
+        "        </div>\n",
+        "        \"\"\"))\n",
+        "        return False\n",
+        "\n",
+        "check_gpu()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✓ cuOpt is installed\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check if cuOpt is installed, if not provide installation instructions\n",
+        "try:\n",
+        "    import cuopt\n",
+        "    print(\"✓ cuOpt is installed\")\n",
+        "except ImportError:\n",
+        "    print(\"⚠️ cuOpt is not installed!\")\n",
+        "    print(\"\\nTo install cuOpt, uncomment and run ONE of the following commands:\")\n",
+        "    print(\"  For CUDA 12: %pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu12\")\n",
+        "    print(\"  For CUDA 13: %pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu13\")\n",
+        "    print(\"\\nThen restart the kernel and run again.\")\n",
+        "    raise ImportError(\"cuOpt is required. Please install it using the instructions above.\")\n",
+        "\n",
+        "# Uncomment ONE of the following lines to install cuOpt (requires GPU with CUDA):\n",
+        "# %pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu12 nvidia-nvjitlink-cu12\n",
+        "# %pip install --upgrade --extra-index-url=https://pypi.nvidia.com cuopt-cu13 nvidia-nvjitlink-cu13\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: matplotlib in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (3.10.7)\n",
+            "Requirement already satisfied: seaborn in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (0.13.2)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (1.3.3)\n",
+            "Requirement already satisfied: cycler>=0.10 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (4.61.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (1.4.9)\n",
+            "Requirement already satisfied: numpy>=1.23 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (2.2.6)\n",
+            "Requirement already satisfied: packaging>=20.0 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (25.0)\n",
+            "Requirement already satisfied: pillow>=8 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (12.0.0)\n",
+            "Requirement already satisfied: pyparsing>=3 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (3.2.5)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\n",
+            "Requirement already satisfied: pandas>=1.2 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from seaborn) (2.3.3)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
+            "Requirement already satisfied: six>=1.5 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install matplotlib seaborn\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Import Required Libraries\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✓ All imports successful\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import time\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from cuopt.linear_programming.problem import Problem, VType, sense, LinearExpression\n",
+        "from cuopt.linear_programming.solver_settings import SolverSettings\n",
+        "\n",
+        "# Set style for better visualizations\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "plt.rcParams['figure.figsize'] = (12, 6)\n",
+        "\n",
+        "print(\"✓ All imports successful\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Problem Statement\n",
+        "\n",
+        "- We have `num_dcs` candidate distribution centers (DCs) with weekly pallet-handling capacities and fixed operating costs.\n",
+        "- We have `num_customers` customers, each with a weekly pallet demand and a location.\n",
+        "- Transportation cost is proportional to distance between DC and customer.\n",
+        "- We must:\n",
+        "  - Decide which DCs to open.\n",
+        "  - Assign every customer's demand to exactly one open DC.\n",
+        "- Objective: minimize total annualized logistics cost = fixed DC cost + transportation cost.\n",
+        "- Constraints:\n",
+        "  - Each customer's demand is fully assigned to a single DC.\n",
+        "  - The sum of assigned demand at each DC does not exceed that DC's capacity.\n",
+        "  - No customer can be assigned to a closed DC.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Problem Data Setup\n",
+        "\n",
+        "Generate a synthetic instance with candidate DC locations and customer locations.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Problem Size: 5 DCs, 20 customers\n",
+            "Total demand: 3244 pallets/week\n",
+            "\n",
+            "DC capacities: [ 970.  898.  932. 1005.  957.]\n",
+            "Fixed costs: $[102321. 101513. 102607.  98348.  96834.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Synthetic instance generation\n",
+        "# -----------------------------\n",
+        "\n",
+        "rng = np.random.default_rng(42)\n",
+        "\n",
+        "# Sets\n",
+        "num_dcs = 5\n",
+        "num_customers = 20\n",
+        "\n",
+        "I = list(range(num_dcs))\n",
+        "J = list(range(num_customers))\n",
+        "\n",
+        "# DC locations on a 2D grid (for visualization)\n",
+        "dc_coords = rng.uniform(low=0.0, high=1000.0, size=(num_dcs, 2))\n",
+        "\n",
+        "# Customer locations\n",
+        "cust_coords = rng.uniform(low=0.0, high=1000.0, size=(num_customers, 2))\n",
+        "\n",
+        "# Euclidean distances and cost per pallet\n",
+        "dist_matrix = np.linalg.norm(\n",
+        "    dc_coords[:, None, :] - cust_coords[None, :, :],\n",
+        "    axis=2\n",
+        ")\n",
+        "\n",
+        "alpha = 0.05  # $ per pallet-km\n",
+        "unit_cost = alpha * dist_matrix\n",
+        "\n",
+        "# Weekly customer demand (pallets)\n",
+        "demand = rng.integers(low=80, high=250, size=num_customers).astype(float)\n",
+        "\n",
+        "# DC capacity: scaled so that total capacity > total demand\n",
+        "total_demand = demand.sum()\n",
+        "avg_capacity = total_demand * 1.4 / num_dcs\n",
+        "capacity = avg_capacity * rng.uniform(low=0.8, high=1.2, size=num_dcs)\n",
+        "capacity = capacity.astype(float)\n",
+        "\n",
+        "# Fixed opening costs (proportional to capacity + noise)\n",
+        "base_fc = 80000.0\n",
+        "fixed_cost = base_fc + 20.0 * capacity + rng.normal(loc=0.0, scale=5000.0, size=num_dcs)\n",
+        "fixed_cost = np.maximum(fixed_cost, 0.0).astype(float)  # Ensure non-negative costs\n",
+        "\n",
+        "# Allowed arcs: all DC–customer pairs\n",
+        "A = [(i, j) for i in I for j in J]\n",
+        "\n",
+        "print(f\"Problem Size: {num_dcs} DCs, {num_customers} customers\")\n",
+        "print(f\"Total demand: {total_demand:.0f} pallets/week\")\n",
+        "print(f\"\\nDC capacities: {capacity.round(0)}\")\n",
+        "print(f\"Fixed costs: ${fixed_cost.round(0)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1500x500 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Visualize problem data\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+        "\n",
+        "# Plot 1: DC and Customer locations\n",
+        "ax1 = axes[0]\n",
+        "ax1.scatter(cust_coords[:, 0], cust_coords[:, 1], c=\"tab:blue\", s=demand/2, alpha=0.6, label=\"Customers\")\n",
+        "ax1.scatter(dc_coords[:, 0], dc_coords[:, 1], c=\"tab:red\", marker=\"s\", s=100, label=\"DCs\")\n",
+        "\n",
+        "for i, (xi, yi) in enumerate(dc_coords):\n",
+        "    ax1.text(xi + 15, yi + 15, f\"DC {i}\", fontsize=9, color=\"red\")\n",
+        "\n",
+        "ax1.set_xlabel(\"X coordinate\")\n",
+        "ax1.set_ylabel(\"Y coordinate\")\n",
+        "ax1.set_title(\"DC and Customer Locations\\n(Customer size = demand)\", fontsize=14, fontweight=\"bold\")\n",
+        "ax1.legend()\n",
+        "ax1.grid(True, alpha=0.3)\n",
+        "\n",
+        "# Plot 2: DC capacity and fixed cost\n",
+        "ax2 = axes[1]\n",
+        "x_pos = np.arange(num_dcs)\n",
+        "width = 0.35\n",
+        "\n",
+        "bars1 = ax2.bar(x_pos - width/2, capacity, width, label='Capacity (pallets)', color='tab:blue', alpha=0.7)\n",
+        "ax2_twin = ax2.twinx()\n",
+        "bars2 = ax2_twin.bar(x_pos + width/2, fixed_cost/1000, width, label='Fixed Cost ($K)', color='tab:orange', alpha=0.7)\n",
+        "\n",
+        "ax2.set_xlabel('DC')\n",
+        "ax2.set_ylabel('Capacity (pallets/week)', color='tab:blue')\n",
+        "ax2_twin.set_ylabel('Fixed Cost ($K)', color='tab:orange')\n",
+        "ax2.set_title('DC Capacity and Fixed Costs', fontsize=14, fontweight='bold')\n",
+        "ax2.set_xticks(x_pos)\n",
+        "ax2.set_xticklabels([f'DC {i}' for i in I])\n",
+        "ax2.legend(loc='upper left')\n",
+        "ax2_twin.legend(loc='upper right')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Problem Formulation\n",
+        "\n",
+        "### Decision Variables\n",
+        "- `y[i]`: Binary, 1 if DC i is open, 0 otherwise\n",
+        "- `x[i,j]`: Binary, 1 if customer j is assigned to DC i, 0 otherwise\n",
+        "\n",
+        "### Objective Function\n",
+        "Minimize: Fixed DC costs + Transportation costs\n",
+        "\n",
+        "$$\\min \\sum_i f_i y_i + \\sum_{i,j} c_{ij} d_j x_{ij}$$\n",
+        "\n",
+        "### Constraints\n",
+        "1. **Assignment**: Each customer assigned to exactly one DC: $\\sum_i x_{ij} = 1$ for each customer $j$\n",
+        "2. **Capacity/Linking**: DC load cannot exceed capacity: $\\sum_j d_j x_{ij} \\le u_i y_i$ for each DC $i$\n",
+        "3. **Binary**: $x_{ij} \\in \\{0,1\\}$, $y_i \\in \\{0,1\\}$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Number of variables: 105\n",
+            "  - DC open/close (y): 5\n",
+            "  - Assignment (x): 100\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Problem and variable creation\n",
+        "# -----------------------------\n",
+        "\n",
+        "prob = Problem(\"CFLP\")\n",
+        "\n",
+        "# Decision variables\n",
+        "y = {}  # DC open/close binaries\n",
+        "x = {}  # assignment binaries\n",
+        "\n",
+        "# y_i: 1 if DC i is open\n",
+        "for i in I:\n",
+        "    y[i] = prob.addVariable(\n",
+        "        name=f\"y_{i}\",\n",
+        "        lb=0.0,\n",
+        "        ub=1.0,\n",
+        "        vtype=VType.INTEGER\n",
+        "    )\n",
+        "\n",
+        "# x_ij: 1 if customer j is assigned to DC i\n",
+        "for (i, j) in A:\n",
+        "    x[i, j] = prob.addVariable(\n",
+        "        name=f\"x_{i}_{j}\",\n",
+        "        lb=0.0,\n",
+        "        ub=1.0,\n",
+        "        vtype=VType.INTEGER\n",
+        "    )\n",
+        "\n",
+        "print(f\"Number of variables: {prob.NumVariables}\")\n",
+        "print(f\"  - DC open/close (y): {num_dcs}\")\n",
+        "print(f\"  - Assignment (x): {num_dcs * num_customers}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Objective set: Minimize (Fixed DC Costs + Transportation Costs)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Objective function\n",
+        "# -----------------------------\n",
+        "\n",
+        "obj = LinearExpression([], [], 0.0)\n",
+        "\n",
+        "# Fixed facility costs\n",
+        "for i in I:\n",
+        "    obj += float(fixed_cost[i]) * y[i]\n",
+        "\n",
+        "# Transportation costs: c_ij * d_j * x_ij\n",
+        "for (i, j) in A:\n",
+        "    flow_cost = float(unit_cost[i, j] * demand[j])\n",
+        "    obj += flow_cost * x[i, j]\n",
+        "\n",
+        "prob.setObjective(obj, sense=sense.MINIMIZE)\n",
+        "\n",
+        "print(\"Objective set: Minimize (Fixed DC Costs + Transportation Costs)\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Assignment constraints added: 20\n",
+            "Capacity constraints added: 5\n",
+            "\n",
+            "Total constraints: 25\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Constraints\n",
+        "# -----------------------------\n",
+        "\n",
+        "# 1) Each customer assigned to exactly one DC\n",
+        "for j in J:\n",
+        "    expr = LinearExpression([], [], 0.0)\n",
+        "    for i in I:\n",
+        "        expr += x[i, j]\n",
+        "    prob.addConstraint(expr == 1.0, name=f\"assign_{j}\")\n",
+        "\n",
+        "print(f\"Assignment constraints added: {len(J)}\")\n",
+        "\n",
+        "# 2) DC capacity and logical linking: sum_j d_j x_ij <= capacity[i] * y_i\n",
+        "for i in I:\n",
+        "    expr = LinearExpression([], [], 0.0)\n",
+        "    for j in J:\n",
+        "        expr += float(demand[j]) * x[i, j]\n",
+        "    expr -= float(capacity[i]) * y[i]\n",
+        "    prob.addConstraint(expr <= 0.0, name=f\"capacity_{i}\")\n",
+        "\n",
+        "print(f\"Capacity constraints added: {len(I)}\")\n",
+        "print(f\"\\nTotal constraints: {prob.NumConstraints}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Solve the MILP with cuOpt\n",
+        "\n",
+        "We now configure a few basic MILP parameters and call `solve()`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Starting optimization...\n",
+            "Problem size: 105 variables, 25 constraints\n",
+            "\n",
+            "Setting parameter time_limit to 3.000000e+02\n",
+            "Setting parameter mip_relative_gap to 1.000000e-04\n",
+            "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: x86_64, device archs: 70-real,75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
+            "CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, threads (physical/logical): 40/80, RAM: 279.15 GiB\n",
+            "CUDA 12.9, device: Tesla V100-SXM2-32GB (ID 0), VRAM: 31.74 GiB\n",
+            "CUDA device UUID: ffffffc2fffffffeffffffce48-7c2a-ffff\n",
+            "\n",
+            "Solving a problem with 25 constraints, 105 variables (105 integers), and 205 nonzeros\n",
+            "Problem scaling:\n",
+            "Objective coefficents range:          [2e+02, 1e+05]\n",
+            "Constraint matrix coefficients range: [1e+00, 1e+03]\n",
+            "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+            "Variable bounds range:                [0e+00, 1e+00]\n",
+            "\n",
+            "Original problem: 25 constraints, 105 variables, 205 nonzeros\n",
+            "Calling Papilo presolver\n",
+            "Presolve status: did not result in any changes\n",
+            "Presolve removed: 0 constraints, 0 variables, 0 nonzeros\n",
+            "Presolved problem: 25 constraints, 105 variables, 205 nonzeros\n",
+            "Papilo presolve time: 0.079425\n",
+            "Objective offset 0.000000 scaling_factor 1.000000\n",
+            "Running presolve!\n",
+            "After trivial presolve: 25 constraints, 105 variables, objective offset 0.000000.\n",
+            "Using 79 CPU threads for B&B\n",
+            "Solving LP root relaxation\n",
+            "Scaling matrix. Maximum column norm 1.000000e+00\n",
+            "Dual Simplex Phase 1\n",
+            "Dual feasible solution found.\n",
+            "Dual Simplex Phase 2\n",
+            " Iter     Objective           Num Inf.  Sum Inf.     Perturb  Time\n",
+            "    1 +1.5483404471657623e+03      20 3.80241542e+00 0.00e+00 0.01\n",
+            "\n",
+            "Root relaxation solution found in 40 iterations and 0.01s\n",
+            "Root relaxation objective +3.72800766e+05\n",
+            "\n",
+            "Strong branching using 79 threads and 7 fractional variables\n",
+            "Exploring the B&B tree using 19 best-first threads and 60 diving threads (79 threads)\n",
+            " | Explored | Unexplored |    Objective    |     Bound     | Depth | Iter/Node |   Gap    |  Time  |\n",
+            "B       175          107    +4.582990e+05    +3.728008e+05        8   2.5e+00     18.7%      0.02\n",
+            "B       359          233    +4.582492e+05    +3.728008e+05        9   2.4e+00     18.6%      0.02\n",
+            "B       359          257    +4.447577e+05    +3.728008e+05        9   2.5e+00     16.2%      0.02\n",
+            "B       522          372    +4.437249e+05    +3.759333e+05       16   2.6e+00     15.3%      0.02\n",
+            "B       531          369    +4.431174e+05    +3.759333e+05       14   2.6e+00     15.2%      0.02\n",
+            "B       561          368    +4.429949e+05    +3.764332e+05       16   2.5e+00     15.0%      0.02\n",
+            "D       686          362    +4.428134e+05    +3.770924e+05        1   3.0e+00     14.8%      0.02\n",
+            "B       996          326    +4.427737e+05    +3.861906e+05        9   3.5e+00     12.8%      0.02\n",
+            "D      1229          310    +4.424911e+05    +3.873834e+05        0   3.6e+00     12.5%      0.03\n",
+            "D      1437          283    +4.424627e+05    +3.877744e+05        0   3.7e+00     12.4%      0.03\n",
+            "D      1575          269    +4.420984e+05    +3.884250e+05        1   3.7e+00     12.1%      0.03\n",
+            "B      1591          271    +4.416565e+05    +3.884366e+05       19   3.7e+00     12.1%      0.03\n",
+            "B      2294           72    +4.412745e+05    +4.039022e+05       14   3.7e+00      8.5%      0.03\n",
+            "Explored 2322 nodes in 0.03s.\n",
+            "Absolute Gap 0.000000e+00 Objective 4.4127454429334094e+05 Lower Bound 4.4127454429334094e+05\n",
+            "Optimal solution found.\n",
+            "Post-solve status: succeeded\n",
+            "Solution objective: 441274.544293 , relative_mip_gap 0.000000 solution_bound 441274.544293 presolve_time 0.093789 total_solve_time 0.362014 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 2322 simplex_iterations 8559\n",
+            "\n",
+            "Optimization completed in 0.481 seconds\n",
+            "Solve status: 1\n",
+            "Best objective value: $441,274.54\n",
+            "Solve time (internal): 0.362s\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Solve\n",
+        "# -----------------------------\n",
+        "\n",
+        "settings = SolverSettings()\n",
+        "\n",
+        "# Example tuning parameters (adjust according to your environment)\n",
+        "settings.set_parameter(\"time_limit\", \"300.0\")          # seconds\n",
+        "settings.set_parameter(\"mip_relative_gap\", \"1e-4\")     # relative optimality gap\n",
+        "\n",
+        "print(\"Starting optimization...\")\n",
+        "print(f\"Problem size: {prob.NumVariables} variables, {prob.NumConstraints} constraints\")\n",
+        "print()\n",
+        "\n",
+        "start_time = time.time()\n",
+        "prob.solve(settings=settings)\n",
+        "solve_time = time.time() - start_time\n",
+        "\n",
+        "print(f\"\\nOptimization completed in {solve_time:.3f} seconds\")\n",
+        "print(f\"Solve status: {prob.Status}\")\n",
+        "print(f\"Best objective value: ${prob.ObjValue:,.2f}\")\n",
+        "print(f\"Solve time (internal): {prob.SolveTime:.3f}s\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extract and Analyze the Solution\n",
+        "\n",
+        "We inspect which DCs are opened, how customers are assigned, and the resulting loads on each DC.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Open DCs: [0, 2, 3, 4]\n",
+            "\n",
+            "DC loads: [925.   0. 420. 946. 953.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Solution extraction\n",
+        "# -----------------------------\n",
+        "\n",
+        "# Status codes: 1 = Optimal, 2 = Feasible, others indicate issues\n",
+        "# See cuOpt documentation for full status code reference\n",
+        "if prob.Status not in (1, 2):\n",
+        "    status_msg = {0: \"Unknown\", 3: \"Infeasible\", 4: \"Unbounded\", 5: \"Time Limit\"}.get(prob.Status, \"Unknown\")\n",
+        "    raise RuntimeError(f\"Solver did not find a solution: Status = {prob.Status} ({status_msg})\")\n",
+        "\n",
+        "# Open DCs\n",
+        "open_dcs = [i for i in I if y[i].getValue() > 0.5]\n",
+        "print(\"Open DCs:\", open_dcs)\n",
+        "\n",
+        "# Assignment matrix\n",
+        "assign_matrix = np.zeros((num_dcs, num_customers))\n",
+        "for (i, j) in A:\n",
+        "    val = x[i, j].getValue()\n",
+        "    assign_matrix[i, j] = val\n",
+        "\n",
+        "# For each customer j, identify assigned DC i\n",
+        "assigned_dc = assign_matrix.argmax(axis=0)\n",
+        "\n",
+        "# Load per DC\n",
+        "dc_load = assign_matrix @ demand\n",
+        "\n",
+        "print(f\"\\nDC loads: {dc_load.round(0)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "======================================================================\n",
+            "                    DC SUMMARY\n",
+            "======================================================================\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>DC</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>Capacity</th>\n",
+              "      <th>Load</th>\n",
+              "      <th>Utilization %</th>\n",
+              "      <th>Fixed Cost ($)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>970.0</td>\n",
+              "      <td>925.0</td>\n",
+              "      <td>95.4</td>\n",
+              "      <td>102321.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>No</td>\n",
+              "      <td>898.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>101513.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>932.0</td>\n",
+              "      <td>420.0</td>\n",
+              "      <td>45.1</td>\n",
+              "      <td>102607.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1005.0</td>\n",
+              "      <td>946.0</td>\n",
+              "      <td>94.2</td>\n",
+              "      <td>98348.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>957.0</td>\n",
+              "      <td>953.0</td>\n",
+              "      <td>99.6</td>\n",
+              "      <td>96834.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   DC Open  Capacity   Load  Utilization %  Fixed Cost ($)\n",
+              "0   0  Yes     970.0  925.0           95.4        102321.0\n",
+              "1   1   No     898.0    0.0            0.0        101513.0\n",
+              "2   2  Yes     932.0  420.0           45.1        102607.0\n",
+              "3   3  Yes    1005.0  946.0           94.2         98348.0\n",
+              "4   4  Yes     957.0  953.0           99.6         96834.0"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "======================================================================\n",
+            "                    CUSTOMER ASSIGNMENTS\n",
+            "======================================================================\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Customer</th>\n",
+              "      <th>Demand</th>\n",
+              "      <th>Assigned DC</th>\n",
+              "      <th>Transport Cost ($)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>221.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3103.969202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>113.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>694.284083</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>216.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4171.780505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>81.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2331.171666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>215.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1807.165698</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>5</td>\n",
+              "      <td>213.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>914.111449</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>6</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2494.399882</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>7</td>\n",
+              "      <td>193.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2357.300628</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>8</td>\n",
+              "      <td>160.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4003.817145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>9</td>\n",
+              "      <td>199.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2971.914652</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>10</td>\n",
+              "      <td>127.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1156.862426</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>11</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2260.513784</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>12</td>\n",
+              "      <td>174.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3423.683757</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>13</td>\n",
+              "      <td>158.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>200.529261</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>14</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1985.308388</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>15</td>\n",
+              "      <td>176.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2880.450359</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>16</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>629.558633</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>17</td>\n",
+              "      <td>103.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>378.718005</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>18</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1850.279653</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>19</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1548.340447</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Customer  Demand  Assigned DC  Transport Cost ($)\n",
+              "0          0   221.0            2         3103.969202\n",
+              "1          1   113.0            3          694.284083\n",
+              "2          2   216.0            4         4171.780505\n",
+              "3          3    81.0            4         2331.171666\n",
+              "4          4   215.0            3         1807.165698\n",
+              "5          5   213.0            0          914.111449\n",
+              "6          6   212.0            3         2494.399882\n",
+              "7          7   193.0            0         2357.300628\n",
+              "8          8   160.0            0         4003.817145\n",
+              "9          9   199.0            2         2971.914652\n",
+              "10        10   127.0            3         1156.862426\n",
+              "11        11   212.0            4         2260.513784\n",
+              "12        12   174.0            0         3423.683757\n",
+              "13        13   158.0            4          200.529261\n",
+              "14        14   165.0            4         1985.308388\n",
+              "15        15   176.0            3         2880.450359\n",
+              "16        16    86.0            0          629.558633\n",
+              "17        17   103.0            3          378.718005\n",
+              "18        18   121.0            4         1850.279653\n",
+              "19        19    99.0            0         1548.340447"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Create summary DataFrames\n",
+        "dc_df = pd.DataFrame({\n",
+        "    \"DC\": I,\n",
+        "    \"Open\": [\"Yes\" if i in open_dcs else \"No\" for i in I],\n",
+        "    \"Capacity\": capacity.round(0),\n",
+        "    \"Load\": dc_load.round(0),\n",
+        "    \"Utilization %\": (dc_load / capacity * 100).round(1),\n",
+        "    \"Fixed Cost ($)\": fixed_cost.round(0)\n",
+        "})\n",
+        "\n",
+        "cust_df = pd.DataFrame({\n",
+        "    \"Customer\": J,\n",
+        "    \"Demand\": demand.round(0),\n",
+        "    \"Assigned DC\": assigned_dc,\n",
+        "    \"Transport Cost ($)\": [unit_cost[assigned_dc[j], j] * demand[j] for j in J]\n",
+        "})\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*70)\n",
+        "print(\" \" * 20 + \"DC SUMMARY\")\n",
+        "print(\"=\"*70)\n",
+        "display(dc_df)\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*70)\n",
+        "print(\" \" * 20 + \"CUSTOMER ASSIGNMENTS\")\n",
+        "print(\"=\"*70)\n",
+        "display(cust_df)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "======================================================================\n",
+            "                    COST BREAKDOWN\n",
+            "======================================================================\n",
+            "\n",
+            "📊 Cost Summary:\n",
+            "   • Total Fixed Costs: $400,110.38\n",
+            "   • Total Transport Costs: $41,164.16\n",
+            "   • Total Logistics Cost: $441,274.54\n",
+            "   • Fixed Cost %: 90.7%\n",
+            "   • Transport Cost %: 9.3%\n",
+            "\n",
+            "🏭 Network Design:\n",
+            "   • DCs Opened: 4 of 5\n",
+            "   • Customers Served: 20\n",
+            "   • Total Demand Met: 3,244 pallets/week\n",
+            "\n",
+            "📦 Utilization:\n",
+            "   • Average DC Utilization: 83.5%\n",
+            "   • DC 0: 95.4% (925/970)\n",
+            "   • DC 2: 45.1% (420/932)\n",
+            "   • DC 3: 94.2% (946/1005)\n",
+            "   • DC 4: 99.6% (953/957)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Calculate cost breakdown\n",
+        "total_fixed_cost = sum(fixed_cost[i] for i in open_dcs)\n",
+        "total_transport_cost = sum(unit_cost[assigned_dc[j], j] * demand[j] for j in J)\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*70)\n",
+        "print(\" \" * 20 + \"COST BREAKDOWN\")\n",
+        "print(\"=\"*70)\n",
+        "print(f\"\\n📊 Cost Summary:\")\n",
+        "print(f\"   • Total Fixed Costs: ${total_fixed_cost:,.2f}\")\n",
+        "print(f\"   • Total Transport Costs: ${total_transport_cost:,.2f}\")\n",
+        "print(f\"   • Total Logistics Cost: ${prob.ObjValue:,.2f}\")\n",
+        "if prob.ObjValue > 0:\n",
+        "    print(f\"   • Fixed Cost %: {total_fixed_cost/prob.ObjValue*100:.1f}%\")\n",
+        "    print(f\"   • Transport Cost %: {total_transport_cost/prob.ObjValue*100:.1f}%\")\n",
+        "\n",
+        "print(f\"\\n🏭 Network Design:\")\n",
+        "print(f\"   • DCs Opened: {len(open_dcs)} of {num_dcs}\")\n",
+        "print(f\"   • Customers Served: {num_customers}\")\n",
+        "print(f\"   • Total Demand Met: {total_demand:,.0f} pallets/week\")\n",
+        "\n",
+        "print(f\"\\n📦 Utilization:\")\n",
+        "if open_dcs:\n",
+        "    avg_util = np.mean([dc_load[i]/capacity[i]*100 for i in open_dcs])\n",
+        "    print(f\"   • Average DC Utilization: {avg_util:.1f}%\")\n",
+        "    for i in open_dcs:\n",
+        "        print(f\"   • DC {i}: {dc_load[i]/capacity[i]*100:.1f}% ({dc_load[i]:.0f}/{capacity[i]:.0f})\")\n",
+        "else:\n",
+        "    print(\"   • No DCs opened (unexpected - check solver status)\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize the Network Design\n",
+        "\n",
+        "We now plot DCs, customers, and assignment arcs in a 2D plane (using the synthetic coordinates defined above).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1600x1200 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Create comprehensive visualizations\n",
+        "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+        "\n",
+        "# Plot 1: Network design with assignments\n",
+        "ax1 = axes[0, 0]\n",
+        "\n",
+        "# Draw assignment lines\n",
+        "for j in J:\n",
+        "    i = assigned_dc[j]\n",
+        "    x1, y1 = dc_coords[i]\n",
+        "    x2, y2 = cust_coords[j]\n",
+        "    ax1.plot([x1, x2], [y1, y2],\n",
+        "             color=\"tab:green\" if i in open_dcs else \"lightgray\",\n",
+        "             linewidth=0.7, alpha=0.7)\n",
+        "\n",
+        "# Plot customers\n",
+        "ax1.scatter(cust_coords[:, 0], cust_coords[:, 1], c=\"tab:blue\", s=demand/2, alpha=0.6, label=\"Customers\")\n",
+        "for j, (xj, yj) in enumerate(cust_coords):\n",
+        "    ax1.text(xj + 5, yj + 5, str(j), fontsize=8, color=\"blue\")\n",
+        "\n",
+        "# Plot DCs\n",
+        "colors = [\"tab:red\" if i in open_dcs else \"gray\" for i in I]\n",
+        "ax1.scatter(dc_coords[:, 0], dc_coords[:, 1], c=colors, marker=\"s\", s=80, label=\"DCs\")\n",
+        "\n",
+        "for i, (xi, yi) in enumerate(dc_coords):\n",
+        "    label = f\"DC {i}\"\n",
+        "    if i in open_dcs:\n",
+        "        label += \" ✓\"\n",
+        "    ax1.text(xi + 5, yi + 5, label, fontsize=8, color=\"red\" if i in open_dcs else \"gray\")\n",
+        "\n",
+        "ax1.set_xlabel(\"X coordinate\")\n",
+        "ax1.set_ylabel(\"Y coordinate\")\n",
+        "ax1.set_title(\"CFLP Network Design Solution\", fontsize=14, fontweight=\"bold\")\n",
+        "ax1.legend()\n",
+        "ax1.grid(True, alpha=0.3)\n",
+        "\n",
+        "# Plot 2: DC utilization\n",
+        "ax2 = axes[0, 1]\n",
+        "dc_colors = ['tab:green' if i in open_dcs else 'lightgray' for i in I]\n",
+        "bars = ax2.bar([f'DC {i}' for i in I], dc_load, color=dc_colors, alpha=0.7, label='Load')\n",
+        "ax2.bar([f'DC {i}' for i in I], capacity - dc_load, bottom=dc_load, color='white', edgecolor='gray', alpha=0.5, label='Unused')\n",
+        "\n",
+        "# Add capacity line markers\n",
+        "for i, bar in enumerate(bars):\n",
+        "    ax2.hlines(capacity[i], bar.get_x(), bar.get_x() + bar.get_width(), colors='red', linestyles='--', linewidth=2)\n",
+        "\n",
+        "ax2.set_ylabel('Pallets/week')\n",
+        "ax2.set_title('DC Load vs Capacity', fontsize=14, fontweight='bold')\n",
+        "ax2.legend()\n",
+        "ax2.grid(True, alpha=0.3, axis='y')\n",
+        "\n",
+        "# Plot 3: Cost breakdown pie chart\n",
+        "ax3 = axes[1, 0]\n",
+        "cost_labels = ['Fixed Costs', 'Transport Costs']\n",
+        "cost_values = [total_fixed_cost, total_transport_cost]\n",
+        "colors_pie = ['tab:red', 'tab:blue']\n",
+        "ax3.pie(cost_values, labels=cost_labels, autopct='%1.1f%%', colors=colors_pie, startangle=90)\n",
+        "ax3.set_title('Cost Breakdown', fontsize=14, fontweight='bold')\n",
+        "\n",
+        "# Plot 4: Customers per DC\n",
+        "ax4 = axes[1, 1]\n",
+        "customers_per_dc = [sum(1 for j in J if assigned_dc[j] == i) for i in I]\n",
+        "demand_per_dc = dc_load\n",
+        "\n",
+        "x_pos = np.arange(num_dcs)\n",
+        "width = 0.35\n",
+        "bars1 = ax4.bar(x_pos - width/2, customers_per_dc, width, label='# Customers', color='tab:blue', alpha=0.7)\n",
+        "ax4_twin = ax4.twinx()\n",
+        "bars2 = ax4_twin.bar(x_pos + width/2, demand_per_dc, width, label='Demand (pallets)', color='tab:orange', alpha=0.7)\n",
+        "\n",
+        "ax4.set_xlabel('DC')\n",
+        "ax4.set_ylabel('Number of Customers', color='tab:blue')\n",
+        "ax4_twin.set_ylabel('Demand (pallets)', color='tab:orange')\n",
+        "ax4.set_title('Customer and Demand Distribution', fontsize=14, fontweight='bold')\n",
+        "ax4.set_xticks(x_pos)\n",
+        "ax4.set_xticklabels([f'DC {i}' for i in I])\n",
+        "ax4.legend(loc='upper left')\n",
+        "ax4_twin.legend(loc='upper right')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extension: Semi-Relaxation (Fractional Customer Assignments)\n",
+        "\n",
+        "The following section demonstrates a **semi-relaxation** of the MILP:\n",
+        "- **DC open/close decisions (y)**: Remain INTEGER (binary)\n",
+        "- **Customer assignment (x)**: Changed to CONTINUOUS (fractional allowed)\n",
+        "\n",
+        "This allows customers to be partially assigned to multiple DCs, which can provide a lower bound on the optimal MILP solution or be used when fractional assignments are acceptable (e.g., splitting orders across DCs).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Semi-Relaxed Problem - Number of variables: 105\n",
+            "Note: y_i remain INTEGER (facility decisions), x_ij are CONTINUOUS (fractional assignments)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Semi-Relaxation: Fractional assignments allowed\n",
+        "# -----------------------------\n",
+        "\n",
+        "# Create a new problem for the semi-relaxed version\n",
+        "prob_lp = Problem(\"CFLP_SemiRelaxed\")\n",
+        "\n",
+        "# Decision variables\n",
+        "y_lp = {}  # DC open/close - remain INTEGER (binary)\n",
+        "x_lp = {}  # assignment - now CONTINUOUS (fractional allowed)\n",
+        "\n",
+        "# y_i: 1 if DC i is open (still binary/integer - facility decisions remain discrete)\n",
+        "for i in I:\n",
+        "    y_lp[i] = prob_lp.addVariable(\n",
+        "        name=f\"y_lp_{i}\",\n",
+        "        lb=0.0,\n",
+        "        ub=1.0,\n",
+        "        vtype=VType.INTEGER  # Still INTEGER (DC open/close decision)\n",
+        "    )\n",
+        "\n",
+        "# x_ij: Fractional assignment (0 <= x_ij <= 1), now CONTINUOUS\n",
+        "for (i, j) in A:\n",
+        "    x_lp[i, j] = prob_lp.addVariable(\n",
+        "        name=f\"x_lp_{i}_{j}\",\n",
+        "        lb=0.0,\n",
+        "        ub=1.0,\n",
+        "        vtype=VType.CONTINUOUS  # Changed to CONTINUOUS for fractional assignments\n",
+        "    )\n",
+        "\n",
+        "print(\"Semi-Relaxed Problem - Number of variables:\", prob_lp.NumVariables)\n",
+        "print(\"Note: y_i remain INTEGER (facility decisions), x_ij are CONTINUOUS (fractional assignments)\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Semi-relaxed objective set (minimize total cost).\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Objective function for relaxed LP\n",
+        "# -----------------------------\n",
+        "\n",
+        "obj_lp = LinearExpression([], [], 0.0)\n",
+        "\n",
+        "# Fixed facility costs (same as MILP)\n",
+        "for i in I:\n",
+        "    obj_lp += float(fixed_cost[i]) * y_lp[i]\n",
+        "\n",
+        "# Transportation costs: c_ij * d_j * x_ij (same as MILP)\n",
+        "for (i, j) in A:\n",
+        "    flow_cost = float(unit_cost[i, j] * demand[j])\n",
+        "    obj_lp += flow_cost * x_lp[i, j]\n",
+        "\n",
+        "prob_lp.setObjective(obj_lp, sense=sense.MINIMIZE)\n",
+        "\n",
+        "print(\"Semi-relaxed objective set (minimize total cost).\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Assignment constraints added (fractional allowed): 20\n",
+            "Capacity constraints added: 5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Constraints for relaxed LP\n",
+        "# -----------------------------\n",
+        "\n",
+        "# 1) Each customer's demand must be fully assigned (sum of fractional assignments = 1)\n",
+        "for j in J:\n",
+        "    expr_lp = LinearExpression([], [], 0.0)\n",
+        "    for i in I:\n",
+        "        expr_lp += x_lp[i, j]\n",
+        "    prob_lp.addConstraint(expr_lp == 1.0, name=f\"assign_lp_{j}\")\n",
+        "\n",
+        "print(f\"Assignment constraints added (fractional allowed): {len(J)}\")\n",
+        "\n",
+        "# 2) DC capacity and logical linking: sum_j d_j x_ij <= capacity[i] * y_i\n",
+        "for i in I:\n",
+        "    expr_lp = LinearExpression([], [], 0.0)\n",
+        "    for j in J:\n",
+        "        expr_lp += float(demand[j]) * x_lp[i, j]\n",
+        "    expr_lp -= float(capacity[i]) * y_lp[i]\n",
+        "    prob_lp.addConstraint(expr_lp <= 0.0, name=f\"capacity_lp_{i}\")\n",
+        "\n",
+        "print(f\"Capacity constraints added: {len(I)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Setting parameter time_limit to 3.000000e+02\n",
+            "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: x86_64, device archs: 70-real,75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
+            "CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, threads (physical/logical): 40/80, RAM: 278.96 GiB\n",
+            "CUDA 12.9, device: Tesla V100-SXM2-32GB (ID 0), VRAM: 31.74 GiB\n",
+            "CUDA device UUID: ffffffc2fffffffeffffffce48-7c2a-ffff\n",
+            "\n",
+            "Solving a problem with 25 constraints, 105 variables (5 integers), and 205 nonzeros\n",
+            "Problem scaling:\n",
+            "Objective coefficents range:          [2e+02, 1e+05]\n",
+            "Constraint matrix coefficients range: [1e+00, 1e+03]\n",
+            "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+            "Variable bounds range:                [0e+00, 1e+00]\n",
+            "\n",
+            "Original problem: 25 constraints, 105 variables, 205 nonzeros\n",
+            "Calling Papilo presolver\n",
+            "Presolve status: did not result in any changes\n",
+            "Presolve removed: 0 constraints, 0 variables, 0 nonzeros\n",
+            "Presolved problem: 25 constraints, 105 variables, 205 nonzeros\n",
+            "Papilo presolve time: 0.002642\n",
+            "Objective offset 0.000000 scaling_factor 1.000000\n",
+            "Running presolve!\n",
+            "After trivial presolve: 25 constraints, 105 variables, objective offset 0.000000.\n",
+            "Using 7 CPU threads for B&B\n",
+            "Solving LP root relaxation\n",
+            "Scaling matrix. Maximum column norm 1.000000e+00\n",
+            "Dual Simplex Phase 1\n",
+            "Dual feasible solution found.\n",
+            "Dual Simplex Phase 2\n",
+            " Iter     Objective           Num Inf.  Sum Inf.     Perturb  Time\n",
+            "    1 +7.2665688039950906e-13      20 1.07431630e+02 0.00e+00 0.00\n",
+            "\n",
+            "Root relaxation solution found in 40 iterations and 0.00s\n",
+            "Root relaxation objective +3.72800766e+05\n",
+            "\n",
+            "Strong branching using 7 threads and 1 fractional variables\n",
+            "Exploring the B&B tree using 1 best-first threads and 6 diving threads (7 threads)\n",
+            " | Explored | Unexplored |    Objective    |     Bound     | Depth | Iter/Node |   Gap    |  Time  |\n",
+            "B        20            2    +4.405980e+05    +3.728008e+05        4   6.5e+00     15.4%      0.00\n",
+            "Explored 28 nodes in 0.00s.\n",
+            "Absolute Gap 0.000000e+00 Objective 4.4059795365403272e+05 Lower Bound 4.4059795365403272e+05\n",
+            "Optimal solution found.\n",
+            "Post-solve status: succeeded\n",
+            "Solution objective: 440597.953654 , relative_mip_gap 0.000000 solution_bound 440597.953654 presolve_time 0.002730 total_solve_time 0.030602 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 28 simplex_iterations 197\n",
+            "Semi-Relaxed Solve status: 1\n",
+            "Semi-Relaxed Objective value: $440,597.95\n",
+            "Solve time: 0.031s\n",
+            "\n",
+            "Comparison:\n",
+            "  MILP (full integer) objective: $441,274.54\n",
+            "  Semi-relaxed objective:        $440,597.95\n",
+            "  Integrality gap: $676.59 (0.15%)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Solve the semi-relaxed problem\n",
+        "# -----------------------------\n",
+        "\n",
+        "settings_lp = SolverSettings()\n",
+        "settings_lp.set_parameter(\"time_limit\", \"300.0\")\n",
+        "\n",
+        "prob_lp.solve(settings=settings_lp)\n",
+        "\n",
+        "print(f\"Semi-Relaxed Solve status: {prob_lp.Status}\")\n",
+        "print(f\"Semi-Relaxed Objective value: ${prob_lp.ObjValue:,.2f}\")\n",
+        "print(f\"Solve time: {prob_lp.SolveTime:.3f}s\")\n",
+        "\n",
+        "# Compare with MILP solution (only if both solved successfully)\n",
+        "if prob.Status in (1, 2) and prob_lp.Status in (1, 2):\n",
+        "    gap = prob.ObjValue - prob_lp.ObjValue\n",
+        "    print(f\"\\nComparison:\")\n",
+        "    print(f\"  MILP (full integer) objective: ${prob.ObjValue:,.2f}\")\n",
+        "    print(f\"  Semi-relaxed objective:        ${prob_lp.ObjValue:,.2f}\")\n",
+        "    if prob.ObjValue > 0:\n",
+        "        print(f\"  Integrality gap: ${gap:,.2f} ({gap/prob.ObjValue*100:.2f}%)\")\n",
+        "    else:\n",
+        "        print(f\"  Integrality gap: ${gap:,.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Open DCs (relaxed LP): [0, 2, 3, 4]\n",
+            "\n",
+            "Fractional assignments (customer -> DC: fraction):\n",
+            "  Customer 8: DC0: 0.772, DC4: 0.228\n",
+            "  Customer 9: DC2: 0.755, DC4: 0.245\n",
+            "\n",
+            "DC loads (relaxed LP): [970.   0. 371. 946. 957.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Analyze fractional assignment solution\n",
+        "# -----------------------------\n",
+        "\n",
+        "if prob_lp.Status not in (1, 2):\n",
+        "    status_msg = {0: \"Unknown\", 3: \"Infeasible\", 4: \"Unbounded\", 5: \"Time Limit\"}.get(prob_lp.Status, \"Unknown\")\n",
+        "    raise RuntimeError(f\"Semi-relaxed solver did not find a solution: Status = {prob_lp.Status} ({status_msg})\")\n",
+        "\n",
+        "# Open DCs (still integer)\n",
+        "open_dcs_lp = [i for i in I if y_lp[i].getValue() > 0.5]\n",
+        "print(\"Open DCs (relaxed LP):\", open_dcs_lp)\n",
+        "\n",
+        "# Fractional assignment matrix\n",
+        "assign_matrix_lp = np.zeros((num_dcs, num_customers))\n",
+        "for (i, j) in A:\n",
+        "    val = x_lp[i, j].getValue()\n",
+        "    assign_matrix_lp[i, j] = val\n",
+        "\n",
+        "# For each customer, show fractional assignments\n",
+        "print(\"\\nFractional assignments (customer -> DC: fraction):\")\n",
+        "for j in J:\n",
+        "    assignments = [(i, assign_matrix_lp[i, j]) for i in I if assign_matrix_lp[i, j] > 1e-6]\n",
+        "    if len(assignments) > 1:  # Only show customers with split assignments\n",
+        "        assignment_str = \", \".join([f\"DC{i}: {frac:.3f}\" for i, frac in assignments])\n",
+        "        print(f\"  Customer {j}: {assignment_str}\")\n",
+        "\n",
+        "# Load per DC\n",
+        "dc_load_lp = assign_matrix_lp @ demand\n",
+        "print(f\"\\nDC loads (relaxed LP): {dc_load_lp.round(0)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extension: Multi-Period Capacity Expansion\n",
+        "\n",
+        "This section extends the CFLP to a multi-period setting where we decide **when** to open DCs (now vs defer) and handle time-varying demand. Key features:\n",
+        "\n",
+        "- **Multiple time periods**: Plan over T periods (e.g., quarters or years)\n",
+        "- **Deferred opening decisions**: DCs can be opened in any period, not just period 0\n",
+        "- **Time-discounted costs**: Future costs are discounted using a discount factor\n",
+        "- **Time-varying demand**: Customer demand may change over periods\n",
+        "- **Capacity constraints**: DCs can only serve customers after they're opened\n",
+        "- **Objective**: Minimize total discounted cost over all periods\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Multi-period setup:\n",
+            "  Number of periods: 3\n",
+            "  Discount factor: 0.95\n",
+            "  Demand growth rate: 10.0% per period\n",
+            "  Cost inflation: 5.0% per period\n",
+            "\n",
+            "Total demand by period:\n",
+            "  Period 0: 3,244.0 pallets\n",
+            "  Period 1: 3,568.4 pallets\n",
+            "  Period 2: 3,925.2 pallets\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Multi-period problem setup\n",
+        "# -----------------------------\n",
+        "\n",
+        "# Number of time periods\n",
+        "num_periods = 3  # e.g., periods 0, 1, 2 (now, next year, year after)\n",
+        "T = list(range(num_periods))\n",
+        "\n",
+        "# Discount factor (e.g., 0.95 means $1 next period is worth $0.95 today)\n",
+        "discount_factor = 0.95\n",
+        "\n",
+        "# Time-varying demand: demand may grow over time\n",
+        "demand_growth_rate = 1.1  # 10% growth per period\n",
+        "demand_period = {}\n",
+        "for t in T:\n",
+        "    demand_period[t] = demand * (demand_growth_rate ** t)\n",
+        "\n",
+        "# Time-varying fixed costs: opening costs may change over time\n",
+        "cost_inflation = 1.05  # 5% cost increase per period\n",
+        "fixed_cost_period = {}\n",
+        "for t in T:\n",
+        "    fixed_cost_period[t] = fixed_cost * (cost_inflation ** t)\n",
+        "\n",
+        "# Transportation costs may also vary over time\n",
+        "unit_cost_period = {}\n",
+        "for t in T:\n",
+        "    unit_cost_period[t] = unit_cost * (1.0 + 0.02 * t)  # 2% increase per period\n",
+        "\n",
+        "print(f\"Multi-period setup:\")\n",
+        "print(f\"  Number of periods: {num_periods}\")\n",
+        "print(f\"  Discount factor: {discount_factor}\")\n",
+        "print(f\"  Demand growth rate: {(demand_growth_rate - 1) * 100:.1f}% per period\")\n",
+        "print(f\"  Cost inflation: {(cost_inflation - 1) * 100:.1f}% per period\")\n",
+        "print(f\"\\nTotal demand by period:\")\n",
+        "for t in T:\n",
+        "    total_demand_t = demand_period[t].sum()\n",
+        "    print(f\"  Period {t}: {total_demand_t:,.1f} pallets\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Multi-period - Number of variables: 330\n",
+            "  DC opening state variables (y): 15\n",
+            "  DC opening decision variables (z): 15\n",
+            "  Assignment variables (x): 300\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Multi-period problem and variable creation\n",
+        "# -----------------------------\n",
+        "\n",
+        "prob_mp = Problem(\"CFLP_MultiPeriod\")\n",
+        "\n",
+        "# Decision variables\n",
+        "y_mp = {}  # y_mp[i][t]: 1 if DC i is open at the START of period t (state variable)\n",
+        "z_mp = {}  # z_mp[i][t]: 1 if DC i opens specifically in period t (decision variable)\n",
+        "x_mp = {}  # x_mp[i][j][t]: 1 if customer j is assigned to DC i in period t\n",
+        "\n",
+        "# y_mp[i][t]: State variable tracking if DC i is open at start of period t\n",
+        "for i in I:\n",
+        "    y_mp[i] = {}\n",
+        "    for t in T:\n",
+        "        y_mp[i][t] = prob_mp.addVariable(\n",
+        "            name=f\"y_{i}_{t}\",\n",
+        "            lb=0.0,\n",
+        "            ub=1.0,\n",
+        "            vtype=VType.INTEGER\n",
+        "        )\n",
+        "\n",
+        "# z_mp[i][t]: 1 if DC i opens specifically in period t\n",
+        "for i in I:\n",
+        "    z_mp[i] = {}\n",
+        "    for t in T:\n",
+        "        z_mp[i][t] = prob_mp.addVariable(\n",
+        "            name=f\"z_{i}_{t}\",\n",
+        "            lb=0.0,\n",
+        "            ub=1.0,\n",
+        "            vtype=VType.INTEGER\n",
+        "        )\n",
+        "\n",
+        "# x_mp[i][j][t]: assignment in period t\n",
+        "for i in I:\n",
+        "    x_mp[i] = {}\n",
+        "    for j in J:\n",
+        "        x_mp[i][j] = {}\n",
+        "        for t in T:\n",
+        "            x_mp[i][j][t] = prob_mp.addVariable(\n",
+        "                name=f\"x_{i}_{j}_{t}\",\n",
+        "                lb=0.0,\n",
+        "                ub=1.0,\n",
+        "                vtype=VType.INTEGER\n",
+        "            )\n",
+        "\n",
+        "print(f\"Multi-period - Number of variables: {prob_mp.NumVariables}\")\n",
+        "print(f\"  DC opening state variables (y): {num_dcs * num_periods}\")\n",
+        "print(f\"  DC opening decision variables (z): {num_dcs * num_periods}\")\n",
+        "print(f\"  Assignment variables (x): {num_dcs * num_customers * num_periods}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DC opening logic constraints added\n",
+            "Assignment constraints added: 60\n",
+            "Capacity constraints added: 15\n",
+            "\n",
+            "Total constraints: 115\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Multi-period constraints\n",
+        "# -----------------------------\n",
+        "\n",
+        "# 1) DC opening logic constraints\n",
+        "for i in I:\n",
+        "    # Period 0: y_mp[i][0] = z_mp[i][0]\n",
+        "    expr = LinearExpression([], [], 0.0)\n",
+        "    expr += y_mp[i][0]\n",
+        "    expr -= z_mp[i][0]\n",
+        "    prob_mp.addConstraint(expr == 0.0, name=f\"open_logic_{i}_0\")\n",
+        "    \n",
+        "    # Period t > 0: State transition constraints\n",
+        "    for t in T[1:]:\n",
+        "        # Monotonicity: once open, stays open\n",
+        "        expr = LinearExpression([], [], 0.0)\n",
+        "        expr += y_mp[i][t]\n",
+        "        expr -= y_mp[i][t-1]\n",
+        "        prob_mp.addConstraint(expr >= 0.0, name=f\"monotone_{i}_{t}\")\n",
+        "        \n",
+        "        # If opens in period t, must be open\n",
+        "        expr = LinearExpression([], [], 0.0)\n",
+        "        expr += y_mp[i][t]\n",
+        "        expr -= z_mp[i][t]\n",
+        "        prob_mp.addConstraint(expr >= 0.0, name=f\"open_if_opens_{i}_{t}\")\n",
+        "        \n",
+        "        # Can only be open if was open before OR opens now\n",
+        "        expr = LinearExpression([], [], 0.0)\n",
+        "        expr += y_mp[i][t]\n",
+        "        expr -= y_mp[i][t-1]\n",
+        "        expr -= z_mp[i][t]\n",
+        "        prob_mp.addConstraint(expr <= 0.0, name=f\"open_logic_{i}_{t}\")\n",
+        "\n",
+        "# 2) Each DC can open at most once\n",
+        "for i in I:\n",
+        "    expr = LinearExpression([], [], 0.0)\n",
+        "    for t in T:\n",
+        "        expr += z_mp[i][t]\n",
+        "    prob_mp.addConstraint(expr <= 1.0, name=f\"open_once_{i}\")\n",
+        "\n",
+        "print(\"DC opening logic constraints added\")\n",
+        "\n",
+        "# 3) Assignment constraints: Each customer fully assigned in each period\n",
+        "for j in J:\n",
+        "    for t in T:\n",
+        "        expr = LinearExpression([], [], 0.0)\n",
+        "        for i in I:\n",
+        "            expr += x_mp[i][j][t]\n",
+        "        prob_mp.addConstraint(expr == 1.0, name=f\"assign_{j}_{t}\")\n",
+        "\n",
+        "print(f\"Assignment constraints added: {num_customers * num_periods}\")\n",
+        "\n",
+        "# 4) Capacity constraints\n",
+        "for i in I:\n",
+        "    for t in T:\n",
+        "        expr = LinearExpression([], [], 0.0)\n",
+        "        demand_t = demand_period[t]\n",
+        "        for j in J:\n",
+        "            d_jt = demand_t[j]\n",
+        "            expr += float(d_jt) * x_mp[i][j][t]\n",
+        "        expr -= float(capacity[i]) * y_mp[i][t]\n",
+        "        prob_mp.addConstraint(expr <= 0.0, name=f\"capacity_{i}_{t}\")\n",
+        "\n",
+        "print(f\"Capacity constraints added: {num_dcs * num_periods}\")\n",
+        "print(f\"\\nTotal constraints: {prob_mp.NumConstraints}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Multi-period objective set (minimize total discounted cost).\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Multi-period objective function\n",
+        "# -----------------------------\n",
+        "\n",
+        "obj_mp = LinearExpression([], [], 0.0)\n",
+        "\n",
+        "# Fixed opening costs (discounted)\n",
+        "for i in I:\n",
+        "    for t in T:\n",
+        "        discount = discount_factor ** t\n",
+        "        cost_t = fixed_cost_period[t][i]\n",
+        "        obj_mp += float(cost_t * discount) * z_mp[i][t]\n",
+        "\n",
+        "# Transportation costs (discounted)\n",
+        "for i in I:\n",
+        "    for j in J:\n",
+        "        for t in T:\n",
+        "            discount = discount_factor ** t\n",
+        "            demand_t = demand_period[t]\n",
+        "            d_jt = demand_t[j]\n",
+        "            cost_t = unit_cost_period[t][i, j]\n",
+        "            flow_cost = float(cost_t * d_jt * discount)\n",
+        "            obj_mp += flow_cost * x_mp[i][j][t]\n",
+        "\n",
+        "prob_mp.setObjective(obj_mp, sense=sense.MINIMIZE)\n",
+        "\n",
+        "print(\"Multi-period objective set (minimize total discounted cost).\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Starting multi-period optimization...\n",
+            "Problem size: 330 variables, 115 constraints\n",
+            "\n",
+            "Setting parameter time_limit to 6.000000e+02\n",
+            "Setting parameter mip_relative_gap to 1.000000e-04\n",
+            "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: x86_64, device archs: 70-real,75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
+            "CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, threads (physical/logical): 40/80, RAM: 278.96 GiB\n",
+            "CUDA 12.9, device: Tesla V100-SXM2-32GB (ID 0), VRAM: 31.74 GiB\n",
+            "CUDA device UUID: ffffffc2fffffffeffffffce48-7c2a-ffff\n",
+            "\n",
+            "Solving a problem with 115 constraints, 330 variables (330 integers), and 710 nonzeros\n",
+            "Problem scaling:\n",
+            "Objective coefficents range:          [2e+02, 1e+05]\n",
+            "Constraint matrix coefficients range: [1e+00, 1e+03]\n",
+            "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
+            "Variable bounds range:                [0e+00, 1e+00]\n",
+            "\n",
+            "Original problem: 115 constraints, 330 variables, 710 nonzeros\n",
+            "Calling Papilo presolver\n",
+            "Presolve status: reduced the problem\n",
+            "Presolve removed: 5 constraints, 5 variables, 10 nonzeros\n",
+            "Presolved problem: 110 constraints, 325 variables, 700 nonzeros\n",
+            "Papilo presolve time: 0.003391\n",
+            "Objective offset 0.000000 scaling_factor 1.000000\n",
+            "Running presolve!\n",
+            "After trivial presolve: 110 constraints, 325 variables, objective offset 0.000000.\n",
+            "Using 7 CPU threads for B&B\n",
+            "Solving LP root relaxation\n",
+            "Scaling matrix. Maximum column norm 1.545262e+00\n",
+            "Dual Simplex Phase 1\n",
+            "Dual feasible solution found.\n",
+            "Dual Simplex Phase 2\n",
+            " Iter     Objective           Num Inf.  Sum Inf.     Perturb  Time\n",
+            "    1 +1.7584595358888398e+03      60 1.18030442e+01 0.00e+00 0.00\n",
+            "\n",
+            "Root relaxation solution found in 113 iterations and 0.00s\n",
+            "Root relaxation objective +5.37659175e+05\n",
+            "\n",
+            "Strong branching using 7 threads and 29 fractional variables\n",
+            "Exploring the B&B tree using 1 best-first threads and 6 diving threads (7 threads)\n",
+            " | Explored | Unexplored |    Objective    |     Bound     | Depth | Iter/Node |   Gap    |  Time  |\n",
+            "D        77           47    +6.394054e+05    +6.311456e+05       15   1.1e+01      1.3%      0.01\n",
+            "D        83           49    +6.390234e+05    +6.312515e+05       14   1.1e+01      1.2%      0.01\n",
+            "D       123           73    +6.389470e+05    +6.314283e+05       15   1.2e+01      1.2%      0.01\n",
+            "B       148           92    +6.389256e+05    +6.315612e+05       27   1.2e+01      1.2%      0.02\n",
+            "B       188          118    +6.383388e+05    +6.317728e+05       28   1.3e+01      1.0%      0.02\n",
+            "B       212          132    +6.382313e+05    +6.318318e+05       34   1.3e+01      1.0%      0.03\n",
+            "B       244          154    +6.380229e+05    +6.318663e+05       29   1.3e+01      1.0%      0.03\n",
+            "B       290          186    +6.378457e+05    +6.319138e+05       32   1.3e+01      0.9%      0.03\n",
+            "B       424          246    +6.373905e+05    +6.319389e+05       32   1.3e+01      0.9%      0.05\n",
+            "D       439          253    +6.372714e+05    +6.319427e+05       13   1.3e+01      0.8%      0.05\n",
+            "D       465          263    +6.368023e+05    +6.319427e+05       12   1.3e+01      0.8%      0.05\n",
+            "D       553          311    +6.364657e+05    +6.319844e+05       14   1.3e+01      0.7%      0.06\n",
+            "D       564          316    +6.363915e+05    +6.319882e+05       15   1.3e+01      0.7%      0.06\n",
+            "D       580          324    +6.362587e+05    +6.319892e+05       13   1.3e+01      0.7%      0.07\n",
+            "D       595          333    +6.351174e+05    +6.319930e+05       11   1.3e+01      0.5%      0.07\n",
+            "D       639          349    +6.350675e+05    +6.319969e+05       13   1.3e+01      0.5%      0.07\n",
+            "D       653          355    +6.349933e+05    +6.319969e+05       14   1.3e+01      0.5%      0.07\n",
+            "D       679          365    +6.348605e+05    +6.320011e+05       12   1.3e+01      0.5%      0.08\n",
+            "D       796          416    +6.347367e+05    +6.320472e+05       11   1.3e+01      0.4%      0.09\n",
+            "D       833          427    +6.343005e+05    +6.320529e+05       11   1.3e+01      0.4%      0.09\n",
+            "D       888          446    +6.340285e+05    +6.320726e+05       13   1.3e+01      0.3%      0.10\n",
+            "D      1558          634    +6.339331e+05    +6.323445e+05       11   1.3e+01      0.3%      0.17\n",
+            "B      1587          641    +6.338670e+05    +6.323574e+05       25   1.3e+01      0.2%      0.17\n",
+            "D      1663          661    +6.334937e+05    +6.323695e+05       11   1.3e+01      0.2%      0.18\n",
+            "Explored 6591 nodes in 0.67s.\n",
+            "Absolute Gap 5.784958e+01 Objective 6.3349365915504191e+05 Lower Bound 6.3343580957984913e+05\n",
+            "Optimal solution found within relative MIP gap tolerance (1.0e-04)\n",
+            "Post-solve status: succeeded\n",
+            "Solution objective: 633493.659155 , relative_mip_gap 0.000091 solution_bound 633435.809580 presolve_time 0.003488 total_solve_time 2.611201 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 6591 simplex_iterations 44835\n",
+            "\n",
+            "Optimization completed in 2.633 seconds\n",
+            "Multi-period Solve status: 1\n",
+            "Multi-period Objective value (discounted): $633,493.66\n",
+            "Solve time (internal): 2.611s\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Solve multi-period problem\n",
+        "# -----------------------------\n",
+        "\n",
+        "settings_mp = SolverSettings()\n",
+        "settings_mp.set_parameter(\"time_limit\", \"600.0\")  # Longer time limit\n",
+        "settings_mp.set_parameter(\"mip_relative_gap\", \"1e-4\")\n",
+        "\n",
+        "print(\"Starting multi-period optimization...\")\n",
+        "print(f\"Problem size: {prob_mp.NumVariables} variables, {prob_mp.NumConstraints} constraints\")\n",
+        "print()\n",
+        "\n",
+        "start_time = time.time()\n",
+        "prob_mp.solve(settings=settings_mp)\n",
+        "solve_time_mp = time.time() - start_time\n",
+        "\n",
+        "print(f\"\\nOptimization completed in {solve_time_mp:.3f} seconds\")\n",
+        "print(f\"Multi-period Solve status: {prob_mp.Status}\")\n",
+        "print(f\"Multi-period Objective value (discounted): ${prob_mp.ObjValue:,.2f}\")\n",
+        "print(f\"Solve time (internal): {prob_mp.SolveTime:.3f}s\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DC Opening Schedule:\n",
+            "  Period 0: Open DCs [0, 1, 2, 3, 4]\n",
+            "  Period 1: No DCs opened\n",
+            "  Period 2: No DCs opened\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Analyze multi-period solution\n",
+        "# -----------------------------\n",
+        "\n",
+        "if prob_mp.Status not in (1, 2):\n",
+        "    status_msg = {0: \"Unknown\", 3: \"Infeasible\", 4: \"Unbounded\", 5: \"Time Limit\"}.get(prob_mp.Status, \"Unknown\")\n",
+        "    raise RuntimeError(f\"Multi-period solver did not find a solution: Status = {prob_mp.Status} ({status_msg})\")\n",
+        "\n",
+        "# Extract opening decisions by period\n",
+        "opening_schedule = {}\n",
+        "dc_open_period = {}\n",
+        "\n",
+        "for t in T:\n",
+        "    opening_schedule[t] = []\n",
+        "    for i in I:\n",
+        "        if z_mp[i][t].getValue() > 0.5:\n",
+        "            opening_schedule[t].append(i)\n",
+        "            dc_open_period[i] = t\n",
+        "\n",
+        "never_open = [i for i in I if i not in dc_open_period]\n",
+        "\n",
+        "print(\"DC Opening Schedule:\")\n",
+        "for t in T:\n",
+        "    if opening_schedule[t]:\n",
+        "        print(f\"  Period {t}: Open DCs {opening_schedule[t]}\")\n",
+        "    else:\n",
+        "        print(f\"  Period {t}: No DCs opened\")\n",
+        "if never_open:\n",
+        "    print(f\"  Never opened: DCs {never_open}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Multi-Period Cost Summary:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Period</th>\n",
+              "      <th>DCs Opened</th>\n",
+              "      <th>Opening Cost ($)</th>\n",
+              "      <th>Transport Cost ($)</th>\n",
+              "      <th>Total Cost ($)</th>\n",
+              "      <th>Discounted Cost ($)</th>\n",
+              "      <th>Total Demand</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>501622.89</td>\n",
+              "      <td>40034.56</td>\n",
+              "      <td>541657.44</td>\n",
+              "      <td>541657.44</td>\n",
+              "      <td>3244.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>45393.04</td>\n",
+              "      <td>45393.04</td>\n",
+              "      <td>43123.38</td>\n",
+              "      <td>3568.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>53975.44</td>\n",
+              "      <td>53975.44</td>\n",
+              "      <td>48712.83</td>\n",
+              "      <td>3925.24</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Period  DCs Opened  Opening Cost ($)  Transport Cost ($)  Total Cost ($)  \\\n",
+              "0       0           5         501622.89            40034.56       541657.44   \n",
+              "1       1           0              0.00            45393.04        45393.04   \n",
+              "2       2           0              0.00            53975.44        53975.44   \n",
+              "\n",
+              "   Discounted Cost ($)  Total Demand  \n",
+              "0            541657.44       3244.00  \n",
+              "1             43123.38       3568.40  \n",
+              "2             48712.83       3925.24  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Total discounted cost: $633,493.66\n",
+            "Total undiscounted cost: $641,025.92\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Calculate costs by period\n",
+        "costs_by_period = {}\n",
+        "for t in T:\n",
+        "    opening_cost = 0.0\n",
+        "    transport_cost = 0.0\n",
+        "    \n",
+        "    # Opening costs\n",
+        "    for i in opening_schedule[t]:\n",
+        "        cost_t = fixed_cost_period[t][i]\n",
+        "        opening_cost += cost_t\n",
+        "    \n",
+        "    # Transportation costs\n",
+        "    for i in I:\n",
+        "        for j in J:\n",
+        "            if x_mp[i][j][t].getValue() > 0.5:\n",
+        "                demand_t = demand_period[t]\n",
+        "                d_jt = demand_t[j]\n",
+        "                cost_t = unit_cost_period[t][i, j]\n",
+        "                transport_cost += cost_t * d_jt\n",
+        "    \n",
+        "    discount = discount_factor ** t\n",
+        "    costs_by_period[t] = {\n",
+        "        'opening_cost': opening_cost,\n",
+        "        'transport_cost': transport_cost,\n",
+        "        'total_cost': opening_cost + transport_cost,\n",
+        "        'discounted_cost': (opening_cost + transport_cost) * discount\n",
+        "    }\n",
+        "\n",
+        "# Create summary DataFrame\n",
+        "summary_data = []\n",
+        "for t in T:\n",
+        "    summary_data.append({\n",
+        "        'Period': t,\n",
+        "        'DCs Opened': len(opening_schedule[t]),\n",
+        "        'Opening Cost ($)': costs_by_period[t]['opening_cost'],\n",
+        "        'Transport Cost ($)': costs_by_period[t]['transport_cost'],\n",
+        "        'Total Cost ($)': costs_by_period[t]['total_cost'],\n",
+        "        'Discounted Cost ($)': costs_by_period[t]['discounted_cost'],\n",
+        "        'Total Demand': demand_period[t].sum()\n",
+        "    })\n",
+        "\n",
+        "summary_df = pd.DataFrame(summary_data)\n",
+        "print(\"\\nMulti-Period Cost Summary:\")\n",
+        "display(summary_df.round(2))\n",
+        "\n",
+        "print(f\"\\nTotal discounted cost: ${prob_mp.ObjValue:,.2f}\")\n",
+        "print(f\"Total undiscounted cost: ${summary_df['Total Cost ($)'].sum():,.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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16lVkZ2djwoQJ2Ldvn07vCCIiospS8trOxsZG70fts7KyMH78eDGxY2pqiv79+8PS0hI7d+7E7du3odVqMX/+fDRu3BitWrXCgwcPsH37drGOHj16oFGjRsjOzkZSUpLY4xL4by6AXbt24fz58wB0hwoAiiaaio+PFxO2UqkU/fr1Q926dZGeno5bt26VSuZWxOHDh/HKK6/AxcUF+/btE5OZBw8exLZt29CvXz80bdoUfn5+4lNIv/32m9hDWKVS4cCBA2J9JZ9ue1IPP+3k4OAAoOiL9/Hjx4sJWzs7O/Tp0wc2NjaIiorCmTNnoFKpMHXqVDRp0qTMiX3T09ORnp6Otm3bIiAgAGlpaWL95Tl58iTmzZsnPqnl6uqK3r17Izc3F1u2bEFeXh6ys7MxceJE7Nu3DzY2NhAEAdOmTRMTthKJBH369IGbmxv27duH6OjoJzo2Ja/3ynu661nJyMhAVlYW+vXrBycnJ/z222/iNd6aNWswduxYMVn3+eef6yQiO3XqhEaNGuHIkSM4fPhwhbe9adMmMWFbr1499OzZEzKZDHfu3MGlS5dw8eJFnfLz5s3TSdi6uLiga9eusLKywrVr13Do0CFx3ZOc18/KzZs3H3tPUKtWLXz00Uc4f/48du3aJb635LwhxQn7nTt36iRsg4KCEBAQgNTUVERERCA3NxcxMTEIDw8Xx4a+e/cuwsPDxfdYWFjg1VdfhVQqxebNm8WnUcvj5eUFc3Nz5ObmQqVS4ezZs+yMQXpj0paI9Obp6SkmbQsKCpCZmQk7Ozvcu3dPLGNvb49atWo99bZKXsQARcmsslhZWcHKykp87Onh95U0d+5cMck6ePBg9OzZU+wB+uuvv6JVq1bIyMjA5s2bxffMmjULAwYM0Hn9yy+/AAB++umncpO23bp1w+LFiyGRSDBixAi0bdtWvJA6d+5chZO2EokEq1evFhOoJiYm+PnnnwFAvGkBim4YUlNTxdfTp08Xb2QGDhyI4OBgMaGrr9atW6Nr167Yv3+/uKx48pEtW7YAKHr8aNq0aTq9bVeuXCkmbKVSKX7++WedCxSVSoW0tLRyt/v111+LN4kuLi6YO3cugKKEbvFYapcvXxYfNwKApUuX6sQwevRoHD58WEy0Fydtf/vtN7FMnTp1sGnTJvEi+pNPPsGvv/5aoWP0pLZs2SJ+XjY2NtiyZYt4/owaNQpdunQRewVFREToPEZGRERUWUpe29WtW1fviZu2bNmi86TUokWLxCGlRo4cia5duyI3NxeCIGD16tVo1aoVCgsLxWskS0tLLFiwQKcXmiAIYlKkeC6Aq1evitc/Dw8VAEAnQeXp6Ym5c+fqPJqu0WjEL+8ratKkSRgzZgwA4J133kHXrl3FxNymTZvQr18/AEVPiRUnU7dt2yY+an706FHxurVWrVro3LlzhWO4c+cOVq5cKf7+cC/l7t27AygahuHq1asAioZh2LBhA+rWrQsAGDt2LPr164crV66goKAA69atKzX0RLERI0Zg2rRpese3atUqMWFrYWGB33//Hfb29gCAjh07YvTo0QCKkpwREREYOXIk/vnnH50n4saMGYNJkyYBKH2c9aXRaHR6GJc1HMOzVvLprubNm4vDHeTk5OD69evw9vbG/fv3dRKzL7/8Mr788ksARZ9LSEiI+Lnpq+RkgePHjy/VMz05ORmWlpYAinrTlrzWbdSoEdatWwcLCwtx2Z07d8RhT57kvH6W9LknGDVqFLZs2aKTtC1rrpMVK1aIv/fr1w/z588XX7ds2VJsc1u2bMHkyZNRq1YtbN++Xef4LlmyROww0717d50vjcoik8ng6OiIGzduACh6mo5JW9IXk7ZEpLeSk4xVNwqFQmfgfnd3dwQEBODEiRMAgAsXLgAA/vnnH52k5rRp08q9SL106RLy8vJKjeMGFM3sWnxzUKtWLdja2oqPqj38WJE+/Pz8dHq8lhzmoGR9JRO4APDKK6+Iv9epUwcBAQFP1Ltk4cKFWL16NTZs2FDmZHJxcXEYPXo0IiIi4OXlBQA6w0MEBQWVujhRKpXlXjw7OTnp9Op5eFiHrKwsWFpa4vTp0zrLSw6B8LDiXsYPHjzA9evXxeXdu3fXuTl8+eWXqyxpWzL+zMxMnUcEH3bmzBkmbYmIyKiV7PFpZ2enMweAvb09OnTogD179uiUtbGxEROxOTk56NKlC5o2bYo6derA29sbgYGBZfYAfRQvLy/UqlULGRkZiI+PR7du3dCoUSPUrVtXrLO8DgGPU/LaytLSEp06dRK/xC6ZLO7Zsyfmz5+P5ORkpKenY//+/QgODhb3HwD69u37RI9J37x5E1988UWZ6wYMGCBeQ5W8ztBoNOjRo0e5dT7qaayxY8dWKL6S7aB9+/ZiwhYoStra2dmJX9wXl334GrZkx4iHj7O+MjIydO5fSs4rURlkMhlee+018XVZ169A0X1HybhK7qtCoUDPnj0rnLRt0aIFDh48CKAocbxx40Z4enrC09MTAQEBaNasmXhvEhsbq3O/88477+gkbAGIc4QUly+m73n9rOh7T6CPvLw8neHUtm7dKg5j8rDCwkKcPXsWHTp00GmbDg4OOsPSBQQEwN3d/bG9bWvVqiUmbR/VaYXoYfp9ZUpEhKIxsYqZmJiIPQJLjsOVmpr6REnJhzk6Ouq8LitRCADZ2dlib4Wy3lesVq1akMlkOstKPtpVXEdFYhcEQedb55IevhF4uMdIRelbX/HFIFDUs+HhCcPKOz6Po1Ao8M477+DgwYPYt28fvvjiCwwePFhnUrmCggJs2LBBfF3yWOo7IVexR+0vALEHb0U+r+ILpJLtBYDOjURZr8vz8OeoUqn0jqXYk8RPRERU2Upe2yUmJup97VLy37WyHqEvuazkNcuCBQvEMV/v37+PAwcO4KeffsLUqVPx0ksv6TyarA8TExMsXLhQnMjz5s2b2Lt3L5YvX44PPvgAHTp0wKpVqypUZ7GHrxNK7lN+fr54PaBQKHQSeL/99lupoRGeZDzOh8nlcjg6OqJTp0747rvvxF6IwLO5zrC1tYWtrW2FYnqSdlCyPQCPPs7P0sMTTpXsUVms+HH88t5TzN7eHiYmJuLr8q5fK2NfR4wYgVdeeQUymQwqlQp///03Nm3ahHnz5mHQoEF4+eWXcf/+fQCl28XjrtOf9Lwu6Umvm/W9J9BHVlZWhe7Dis+JkvtU1oTa+nxe1bnzExkWe9oSkV7OnTsnDo0AFD0+UvyoXJs2bcRB/gVBEB9zehotWrTQGaZg79698PHxKVWu5EQOxe8rS0ZGhjgGb7GSkzQUjxX68DfwI0eOhJOTU7lxljfGqFyu++f14dliK+rhi8Py6rO2thZ/f/DgAfLz83UGx3/U8BH6qlOnDurUqYNXXnkFH374Ibp16yYmr0sm9m1sbMRH/x/37fPD9N3fhz+viRMnPnYygIe/jS85nERZr8uL4+EL+OJvzyuiZPyOjo6PnLyhZI8HIiKiytSmTRvx3/TMzEwcOHBAr3FtS/67VvI6q6xlJa9ZfHx8sHPnTsTFxeHixYtITEzExYsXceTIEWi1WqxevRqdOnUqc9LT8gQGBuLAgQO4cOECLl++jBs3buDMmTOIiYmBWq3GF198gc6dO6NOnTp61wkUXSeU/De55D6ZmJjoJJUGDx6MZcuWQa1WIzo6Gps2bRK/PG7UqFGZ17b6aNWqFdauXfvYciU/DxMTE52JsB5W3jXtwx0A9FHyGlDfdlCyPQBFx7nkkGtl1aNPHBKJREyYlZVQrFWrFqRSqZj8K+ua9ebNm+LvUqm03CT2k1yvA6WvPZ9kX+VyOb744guEhYXh9OnTuH79Oq5fv479+/cjMzMTV65cwVdffYX58+eXun6+desWmjVrVm7dT3peP4vrZn2PqT4ebuOdO3d+5DAFjRs3BqC7T2V9uaHP51Uy8V1W4peoPEzaEtFjJSQk4IMPPtBZVjK59Oqrr2LZsmV48OABAODbb78VHz0rSRAE7Nu3D/Xq1UODBg0euc3u3btj/vz5YjJw3bp16NOnj/joPVA03trSpUvF1wqFotzJHNRqNXbt2iU+fnTr1i2dR8aK/1Fu3rw5ZDKZOLaaXC4vczykW7du4fr163o/jlNVmjRpovN6586d4pi8N27cKDWcgD4OHz6MK1euYMCAAaUuMkxMTHQS1CUval588UXs27cPQNHMvadOnRLHlAWKHjtKTU19qhmTH55J2NbWFkOGDClV7urVq+LFkqWlJTw9PcUhEvbt24eJEyeKN1klJ0N5WMn9u379OrKysmBtbY3s7GysX7++wvH7+/uLXzykp6ejXbt2pW7gBEFAdHR0hR8NJSIielJvvPGGOOkpAMycORPu7u6l/o1Sq9XYunUrOnfuDHt7e51/19LS0nD48GHxUerU1FQcOXJEfG/xxEBA0ZBTvr6+8Pb21pnc9OWXX0ZcXByAoqEHipO2Ja898vLySsVfUFCAW7duwcvLC02bNhUnoBIEAS1btkR2dja0Wi0uX75c4aTttm3bxDFtc3JydCZsKr6eLObo6IgePXpgx44dEARBHLcUgM6cCZWl5DEuKChA/fr1dR5tL/bPP/8809nsiyfiBYDIyEikpqaKvUkPHz6sk/gqjvHha9g//vhDHF/04eOsL7lcDldXV/GJvTt37ugcE6DoWtbb21t8bP7gwYOIi4sT2+Hly5d1tu3j4/PUx6pRo0Y6yeQ//vgDHTp0AFB0TpUcQkNfCQkJcHFxgZ2dnc4XLA0bNhR7qhcP3+Hn5we5XC4OkbBixQp06tRJZ8i3e/fuwc7ODgqF4onP62d93fw4D3eaeXgYO3Nzc/j6+oqfdUZGBoYPH14qMZydnY0jR46I96tNmjTB3r17ARQlaKOjo8X73NOnTz+2c4pGoxF7OQPgNT1VCJO2RFRKZGQk0tPTkZOTg0uXLiEyMlJn3KOhQ4ciKChIfG1nZ4dZs2ZhypQpEAQBubm5ePPNN9G2bVv4+flBoVAgKSkJR48exe3bt7FmzZrHxmBpaYlPPvkEkydPBlD0zfiAAQMQHByMOnXq4O7du9i5c6fOt5bvv/+++BhcWaZNm4aYmBhYW1tj+/bt4iRkQNEkXUDRt+0DBgwQxzRdsWIFzp8/D39/f5iYmODevXv4559/cPHiRYSEhKB9+/Z6HtWq0aVLF9jb24vf2H/66ac4e/YsrKyssHXr1gpPQgYUXYwtWLAACxcuhJ+fHxo3bgx7e3vxArrkt8slj8eoUaOwf/9+aLVaaDQajBgxAj179oSnpydSUlIQFRWFoUOHPlWvbB8fH7Rr107s6f3555/jyJEjaNKkCSQSCZKSknDmzBnEx8dj/Pjx4rfpr776qnjjdOPGDQwePBidOnXC1atXxURzWUrOOpyTk4N+/fqhWbNmOH36tM6kLfrq378/vv/+e6Snp6OwsBCvv/46evbsiTp16kClUuH69ev4+++/kZKSgjVr1vAij4iIqkSDBg3w3nvv4euvvwZQ9KTOgAED8NJLL8HX1xcSiQT//vsvoqKikJKSIj7yHxISgqVLl4pfuk+cOBEDBgyApaUlduzYgdzcXAAQJ2otNmjQIDg5OaFFixZwcnISJxstTtgCur3kSn7he+HCBcyePRsuLi5QKBQYPnw4srKyEBwcjAYNGqBp06ZwcnKCqakpTp06pTNM0sM9HvWxcOFCJCQkwNXVFXv37tWZHGvQoEGlyg8bNgw7duwA8N+j90qlEn369KnwtivqpZdegpeXF+Lj4wEA48aNQ/fu3eHl5QVBEPDvv/8iJiYGt2/fRnh4OHx9fZ/JdkeOHIkDBw5AEAQ8ePAAr776Kvr06YPc3FydJ+lq1aqFkJAQAEUdJ4rHNgaAZcuW4fbt23Bzcyt1nCvC399fTNpevHhRZ46LYkOGDMEnn3wCoOgzGjBgABo2bAgAuHLlis49Q1mdAyqqdu3a6NChgzgZ2fbt25GTkwNfX18cOXKkwuPZAsDq1auxfft2tGnTBu7u7nBwcEBmZqbOmK0lnywcNGiQOLnyhQsX0Lt3b3Tp0gXW1tZITEzEn3/+iaioKCgUiic+r5/1dfPjPNwRZPLkyfD394dUKsUrr7wCBwcHjBo1Ch9++CGAooTryy+/jE6dOsHGxgYZGRm4ePEiTp06BScnJ3Eyt759+2Lx4sXikA7jxo3DwIEDIZFIdNpzeeLj48UvlxQKxSN7NRM9jElbIipl165dOjNvFpPL5Rg3bpzYu6Ckvn37QiaTYcaMGcjOzoYgCDh69KiYTHsSffr0gUqlwqxZs5Cfn4+8vLwy/2GUyWR4//33y+wRW8zBwQG1a9fGxo0bS60bMmSIzgRQ06ZNw61bt3Ds2DEAwPHjx3H8+PEn3o+qZGpqijlz5mD8+PEoLCyEWq0W99nGxgZ+fn7iBAEVfbyosLAQMTExiImJKXN9hw4d8PLLL4uv/fz88Nlnn2HWrFlQq9VQq9X4448/nmzHHuHLL7/EqFGjcOnSJWi1Whw6dOixvTFGjBiB/fv3i5NuXLx4Uex90KpVq3Ina+vWrRvq1q0rPjJ6+/Zt8UagY8eOOjMB68PKygpLly7Fu+++i/T0dOTm5lZ4kg0iIqLKEBoaCjMzM3z55ZdQqVQoLCzE/v37xR6UZbG2tsZ3332Hd999F1lZWcjPzy/Vo04qlWLKlCmlZpi/detWuT3W3N3d0bNnT/F1165dsXTpUmi1Wmi1WnGoAHNzc51JO69evVpuAqxZs2Zo2bLlow9CGVq2bIlt27aVWv7SSy+hX79+pZb7+fmhSZMmOpMZde7cWefR/8oil8uxZMkSjBo1Crdv34ZarcbOnTsrfbstW7ZEWFgY5s+fD61Wi6SkJPzwww86ZaysrLBo0SIxcS6RSDB37lyMGDECubm5EARBfPqpuLfnoyZLK0+7du3EpHl5k2QNHDgQZ86cEa/B1Gq1OElxSf37938m4xADwCeffILz58+LHS0OHjwoTiT2qGvRR8nLyyv3GlgqleKtt94SX4eFheH27dviteujOtY86Xn9rK+bH8ff3x+Ojo7icHAHDhwQv1Bq1aoVHBwc0LdvX1y9ehXLly8HUNRDOSEh4ZH1uri4ICwsDJ999hmAoiHoVq9eDaBoojQ7OzudIeIeVrLdtWjRQmfcY6LHYdKWiMokk8lgamoKW1tbeHh4oEWLFhg4cOAjH2UPDg5Gu3btsHnzZkRGRuLKlSvIzMyEVCqFs7MzAgIC0KtXL51H5B+nf//+CAoKwsaNG3H06FEkJiYiJycHZmZm8PDwQJs2bfD666/jhRdeeGQ9JiYmWLNmDRYvXow9e/YgNTUV7u7uGDJkCIYNG6ZT1szMDCtXrsSuXbuwfft2XLhwARkZGZDL5XBycoKvry+CgoLQvXt3vfejKnXq1AmrV6/GokWLcPbsWSiVSrRs2RJTpkzB7NmzxXL69i7p1asX7O3tER0djdjYWCQnJyMtLQ1qtRo2Njbw9vZG7969ERISIo5zXGzgwIEICAjA2rVrceLECdy5cwcajQb29vZo1qxZhdpCeezt7fHrr79i8+bN2LNnD+Li4pCVlQUTExM4OzujSZMm6NChA7p06SK+R6FQ4KeffsJ3332HHTt2IC0tDe7u7hg8eDC6dOmCbt26lbktExMTrF69GvPnz8fRo0dRUFAAHx8fjB49GlZWVk908RkQEICdO3di3bp1OHz4MG7cuIG8vDxYWFjAw8MD/v7+6NKlyxPdWBIRET2N4cOHo2fPnvj1119x7Ngx8RFnhUIBV1dXtGrVCsHBwTqTBbVs2RI7duzA6tWrERkZiVu3bqGwsBCOjo548cUXMWzYMDRv3lxnOzNnzkRMTAwuXLiA5ORkZGVlQalUwsPDA+3bt8eoUaN0etr6+vriq6++wooVK3Dt2rVSk0fZ2NhgxowZOH36NC5fvoyUlBRkZ2fDzMwMdevWRZcuXTBixIhSj1PrY8WKFVi2bBm2b9+Oe/fuwcnJCSEhIQgNDS33C/Fhw4Zh6tSp4uuqGBqhmKenJ7Zv346NGzdi//79SEhIQE5ODkxNTeHu7o5mzZrhpZdeEh/Pf1ZGjhyJF198EWvXrkVMTAzu378PmUwGNzc3tG/fHiNHjiw1Xn+zZs2wYcMGfPXVV2IngebNm2PixIk4evToEyVte/bsidmzZ+PBgwc4deoUUlJSSk0cJZFIEB4eju7du2PLli04e/asOISDnZ0dmjVrhv79+6NTp05PeDRK8/DwwKZNm/DVV1/h6NGjUKlU8PX1RWhoKNLT0yuctH311VdhbW2N2NhY3Lp1C2lpadBqtXBwcEDz5s0xbNgwnfFbTUxMsHz5cuzZswfbtm3D+fPnkZGRARMTE7i6uqJNmzY680Q8yXldGdfNj6JUKvHjjz9iwYIFiI2NRU5OTpnlPvjgA7z00kvYsGEDTp8+jfv370MQBNjZ2aFBgwZo1aoVevXqpfOeoUOHwsHBAcuXL8fVq1dhYWGBDh064IMPPsCUKVMembQtHloBqNpzn54PEoHT2BHRc2rx4sX47rvvABTNPFr87fXzrqCgoMxvcO/du4fg4GDxAub9998vs9c0ERERET07sbGxGDx4MICiR7gPHTqkMzkuVa5Zs2aJQwFMnz69VIcNosqSlpaG9u3bo7CwELa2tjh8+DB72lKFsKctEdFzJjIyEgsWLECfPn1Qt25dmJmZITExEevWrRMTtubm5vyml4iIiKiSFBQUIDY2FllZWfj+++/F5a+//joTtlUsNDQUmzdvRkFBAdatW4ehQ4eWejqMqDKsX79enFNkzJgxTNhShTFpS0T0HLp+/ToWL15c5joLCwt88803cHR0rOKoiIiIiGqG5ORknfF1gaJH4h9eRpXP2dkZw4YNw4oVK5CYmIh9+/bpjJFMVBlKjv3r6ur6TCaxo5qHSVsioueMt7c3Xn/9dXH8sOIxgOvUqYN27dph6NChcHZ2NnSYRERERDWCnZ0d2rRpgw8//BAWFhaGDqdGmjJlCqZMmWLoMKgGMTU1rTaTWZPx4pi2REREREREREREREaEA7kQERERERERERERGREmbYmIiIiIiIiIiIiMCMe0fQytVovCwkJIpVJIJBJDh0NERET0XBIEAVqtFnK5nLN6Gwive4mIiIgqn77XvUzaPkZhYSHOnTtn6DCIiIiIaoSmTZtCqVQaOowaide9RERERFXncde9TNo+RnHGu2nTppDJZAaO5j+CICArKwvW1tbsCUE1Ats81TRs81TTaDQanDt3jr1sDchYr3trouLzgZ8F0aPxXCHSH88X46HvdS+Tto9RfKMsk8mMqlELggCpVAqZTMabeaoR2OappmGbp5qK7d1wjPW6tybjZ0GkH54rRPrj+WI8Hnfdy64MREREREREREREREaESVsiIiIiIiIiIiIiI8KkLREREREREREREZER4Zi2RERERERERERUo2k0GqjVakOHUWk0Gg0AID8/n2PaVjKFQvFMjjGTtkREREREREREVCMJgoC7d+8iIyPD0KFUKkEQIJfLcePGDU78WgVq1aoFZ2fnpzrWTNoSEREREREREVGNVJywdXJygrm5+XOb0BQEAXl5eTAzM3tu99EYCIKA3Nxc3L9/HwDg4uLyxHUxaUtERERERERERDWORqMRE7b29vaGDqdSCYIArVYLU1NTJm0rmZmZGQDg/v37cHJyeuKhEjgRGRERERERERER1TjFY9iam5sbOBJ63hS3qacZJ5lJWyIiIiIiIiIiqrHY85SetWfRppi0JSIiIiIiIiIiIjIiTNoSERERERERERHRUxk2bBjmzJlj6DCeG5yIjIiIiIiIiIiIqATNgwcQCgqqbHsSExPILCwq9J47d+5g0aJFiIyMREZGBhwdHdGlSxeMGzcOtra2lRRp+RYvXgy5vGpSjTdu3MCyZctw9OhRpKWlwcnJCX5+fnjzzTfRtGnTp65/8eLF2L9/P7Zt2/YMon0yTNoSERERERERERGVIBQUIGXp9yi8f6/StyV3qg2Hd8cCFUja3rx5E4MHD0bdunXx9ddfw93dHVevXsWXX36JyMhIbNq0CbVq1aq8oMtQVds7d+4cRo4ciQYNGuCzzz5DvXr18ODBAxw4cADz58/HunXrqiSOysakLRERERFRGRYvXozvvvtOZ5mnpyf27NkDACgoKMC8efOwa9cuqFQqBAUF4dNPP4WDg4NYPikpCTNnzsSJEydgbm6Ofv36YfLkyTq9UE6cOIF58+bh6tWrcHFxwdixY9G/f3+d7a5fvx4rV65EcnIyfHx88Mknn6BZs2bien1iISIiooopvH8P6ttJhg6jTLNmzYJCocBPP/0EU1NTAICrqysaNWqEbt264ZtvvsGsWbMAAJ07d8aAAQMQFxeHI0eOwMrKCmPGjMHQoUPF+rKysjB//nwcOHAAKpUKTZo0wbRp0+Dj4wPgv56nb775JhYtWoTMzEx06NABn3/+OSwtLQEUDY/g4+OD//3vf+J2Bw0ahBs3bmDPnj2wsbHB2LFjMXjwYHG7p0+fxqxZs5CQkIAGDRrg3Xffxbhx47B161b4+vqW2m9BEPDxxx+jTp06+OWXXyCV/jfyq6+vL4YPHy6+jouLw5w5cxAbGwszMzN0794dYWFhsPj/5PiJEyfw5Zdf4tq1a5DL5ahfvz6++uornDhxQrwG9Pb2BgCEh4eXuj6rbBzTloiIiIioHA0aNEBUVJT488svv4jr5s6di0OHDmHhwoVYu3Yt7t+/j/Hjx4vrNRoNQkNDoVarsXHjRsybNw8RERFYtGiRWObmzZsIDQ1F69atsW3bNowYMQLTp09HZGSkWGbXrl0IDw/HuHHjEBERAR8fH4waNQqpqal6x0JERETPj4yMDERFRWHIkCFiwraYo6Mj+vbti927d0MQBHH5ypUr0bBhQ2zZsgWjR4/GnDlzcPToUXH9e++9h9TUVPz444/YsmULGjdujBEjRiAjI0Ms8++//+LAgQNYtmwZli9fjpMnT+LHH398ZKyrVq1CkyZNsHXrVgwZMgQzZ85EQkICACAnJwdjx45Fw4YNERERgffeew9ffvnlI+u7dOkSrl69irfeeksnYVvM2toaAJCbm4tRo0bBxsYGv//+OxYuXIhjx47h888/BwAUFhZi3LhxaNmyJbZv345NmzZh8ODBkEgkCA4OxltvvaVzHRgcHPzIuCoDk7bVmEQiMXQIRFWKbZ5qGrZ5IsOTyWRwdHQUf+zs7AAA2dnZ2Lx5M8LCwhAYGIgmTZpg7ty5OHPmDGJjYwEAUVFRuHbtGr788kv4+vqiY8eOeO+997B+/XqoVCoAwMaNG+Hu7o6wsDB4eXnhjTfeQI8ePbB69WoxhlWrVmHQoEEYMGAA6tevj1mzZsHU1BSbN2/WOxYiIiJ6fty4cQOCIMDLy6vM9V5eXsjMzERaWpq4LCAgAG+++SY8PT0xbNgwneuNmJgYnD17FosWLULTpk1Rt25dTJ06FdbW1ti7d69YhyAICA8PR8OGDdGiRQu8/PLLiI6OfmSsHTp0wNChQ1GnTh288847sLW1xYkTJwAAf/zxBwBg9uzZqF+/Pjp27Ii33377kfUlJiYCAOrVq/fIcjt27IBKpcL8+fPRsGFDBAYGYsaMGdi2bRtSUlKQk5OD7OxsdOrUCS+88AK8vLwQEhICV1dXmJqawtzcXOc68OHkeFXg8AjVmFqmRlJOEm/qqUYQBAGF2kLk5OSwzVONwDZPNY1WozV0CGW6ceMGgoKCYGJiAj8/P0yePBmurq44f/481Go12rZtK5b18vKCq6srYmNj4efnh9jYWDRs2FBniIKgoCDMnDkT165dQ6NGjRAbG4vAwECdbQYFBWHu3LkAAJVKhQsXLiA0NFRcL5VK0bZtW5w5cwYA9IqFqp+qmsiFiIiqr5I9aR/n4esBPz8//PzzzwCKhhHIzc1F69atdcrk5+fj33//FV+7ubmJQyEAgJOTk86TP2UpHl4AKOqU4uDgIL7n+vXr8Pb2homJiVjmWUwiBgDx8fHw9vaGubm5uCwgIABarRbXr19Hy5Yt0b9/f4waNQrt2rVDYGAgevXqBScnp2ey/WeBVwLV2AP1A/x5809kq7MNHQpR5RMAtVoNhUIBMH9FNQHbPNUwNgobNEdzQ4eho1mzZggPD4enpyeSk5OxZMkSDB06FH/88QdSUlKgUCjER/CK2dvbIzk5GQCQkpJSakzZ4tePK5OTk4P8/HxkZmZCo9HA3t6+1HaKHy3UJ5aKEAShQjeBT0qbm1uls3JXK4IALze3KvssiKqr4vOD5wo9KWNrQ/rG4OHhAYlEgmvXrqFr166l1l+7dg02NjawtbUtc99KvhYEAQ8ePICjoyPWrFlTqi5ra2uxDrlcXqourVarcxwf/v+H3yORSHTeU95+l/eZ1KlTB0BRUrasMW8frrOs7RTXPXfuXLzxxhuIjIzErl27sHDhQvz000/w8/Mr8/0V8ai2pW+dTNpWc9mqbGSpswwdBlHlE4p6GykFJRNYVDOwzVMNI4PM0CGU0rFjR/F3Hx8fNG/eHJ06dcLu3bsN8ohcVcnKyipzjLhnSSqVwlSlQsrS76G+V/mzclc38tpOsB09Gjk5OYYOhcioabVFT2lUxd8tej6pVCpotVpoNBpoNBrdlYIAQXjypF1FCELR/5SKoRzW1tYIDAzEhg0bMGzYMJ3rkuTkZOzYsQMvv/yyeI4AEIdMKt7GmTNnUK9ePWg0Gvj4+CAlJQUSiQRubm6ltqfRaMTkY8kYi+t/eNmjXpesp06dOti+fTvy8vKgVCoBAP/8849YZ1nHo2HDhvDy8sKqVavQo0ePUud+VlYWrK2t4enpiYiICGRnZ4u9bWNiYiCVSvHCCy+IdXt7e8Pb2xtvv/02hgwZgj/++ANNmzaFXC4vNwZ9aDQaaLVaZGdno+ChL6lLfi6PwqQtEREREZEerK2tUbduXfz7779o27Yt1Gq1eGNQLDU1FY6OjgCKesyePXtWp46UlBQA0ClTvKxkGUtLS5iamkIqlUImk5V69DA1NVXsoevg4PDYWCq6nzJZ5SfRNenp0KYkQ3v3bqVvq7rR/v8NqKWlZZV8FkTVVXEypar+btHzJz8/H6mpqZDJZKXakEYigURSNfNMSCRF/1ORdjxjxgy8/vrrCA0NxXvvvQd3d3dcu3YNX3zxBZycnPD+++/r1HfmzBmsXr0avXr1wrFjx7Bv3z4sW7YMMpkMQUFB8PPzw8SJE/Hhhx+ibt26uH//Pg4fPoyuXbuiadOmkEgkkDwUY3HC9OFlj3pdsp6XX34Z3377LWbOnInRo0cjKSlJHGdXLpeXezzCw8Px5ptvYvjw4RgzZgzq1auH3NxcHDp0CFFRUVi3bh1eeeUVLFmyBP/73/8wfvx4pKWlYe7cuXj55ZdRu3Zt3Lp1C5s2bULnzp3h5OSE69ev48aNG+jXrx9kMhnc3d1x+/ZtXLlyBc7OzrCwsBATy/qQyWSQSqWwsrIq9WW/volgJm2JiIiIiPTw4MED3Lx5E46OjmjSpAkUCgWio6PRo0cPAEBCQgKSkpLEMeP8/PywbNkypKamisMbHDt2DJaWlqhfv75Y5siRIzrbOXbsmFiHUqlE48aNER0dLT7+qNVqER0djTfeeAMA9IqlIopvpsiA/v/w87MgerTi84PnCj2px7UhuVPtKomjeDsVaceenp7YvHkzFi9ejPfffx+ZmZlwcHBA165dMW7cONja2oplJRIJ3nzzTVy6dAk//vgjLC0tERYWhg4dOojrf/jhByxcuBDTpk1Deno6HBwc0KJFCzg6Ouocn4djLGtdef//8HusrKywbNkyzJw5E/369UPDhg0xbtw4TJ48GSYmJuUej+bNm2Pz5s1YtmwZPvnkE6Snp8PJyQn+/v743//+B4lEAnNzc6xcuRJz5szBwIEDYWZmhu7duyMsLAwSiQRmZma4fv06Jk6ciIyMDDg5OWHo0KF47bXXIJFI0LNnT+zfvx8jRoxAVlYWwsPD0b9/f70/n0e1LX0/Z4lgDIN2GDGNRiNO4GBM39wJgoB/M/7FlvgtHB6BaobiR8WVfFScagi2eaphbJW2aId2RnXNNX/+fHTq1Amurq64f/8+Fi9ejEuXLmHXrl2ws7PDp59+iiNHjiA8PByWlpaYPXs2AGDjxo0Aiq4j+/XrBycnJ0yZMgXJycn46KOPMHDgQHzwwQcAgJs3b6Jv374YMmQIBgwYgOPHj2POnDlYvnw52rdvDwDYtWsXpk6dis8++wzNmjXDzz//jN27d2P37t1ib9vHxaKPqr7uLUxLw92ZM6G+nVTp26pu5G6usJ82DSaOjkZzPhAZI2O9X6fqIz8/H9evX4enp2fp3pAPHlTp2OsSExPILCwqpe7OnTtj+PDhGDhwIMzNzY36S47t27dj2rRpiImJqdbDUT2yben5t4s9bYmIiIiIynD37l188MEHyMjIgJ2dHV588UX8+uuvsLOzAwBMmzYNUqkUEydOhEqlQlBQED799FPx/TKZTOw9MnjwYJiZmSEkJAQTJ04Uy3h4eGD58uUIDw/HmjVr4OzsjNmzZ4sJWwAIDg5GWloaFi1ahOTkZPj6+mLFihU6E5g9LhYiIiKqGJmFBVBJSVT6z9atW+Hu7o7atWsjLi4OCxYsQM+ePat1wvZZYdKWiIiIiKgM33zzzSPXm5iY4NNPP31kctTNzQ0//vjjI+tp3bo1tm7d+sgyb7zxhjgcwpPGQkRERGRskpOTxS+mHR0d0bNnT7z//vuGDssoMGlLRERERERERET0nDp48CAEQUBubq6hQynlnXfewTvvvGPoMIyS1NABEBEREREREREREdF/mLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiIiIiIiIiIjwqQtERERERERERERkRGRGzoAIiIiIiIiIiIiY/KgoBD5ak2Vbc9UIYOFiX5pOm9v70euHz9+PCZMmPAswjIanTt3xvDhwzFy5MjHlr148SKWLVuGmJgYZGdnw8XFBa1atcKoUaPg6en51LGEhYUhKysLS5cufeq6HoVJWyIiIiIiIiIiohLy1RosPngN97LyK31bta1NMaFzfb2TtlFRUeLvu3btwqJFi7Bnzx5xmbm5ufi7IAjQaDSQyWTPLuAqpFKpoFQq9S5/6NAhTJgwAUFBQViwYAE8PDyQlpaGPXv24Ntvv8XChQsrL9hnjElbIiIiIiIiIiKih9zLyset9DxDh1GKo6Oj+LuVlRUkEom47MSJExg+fDh++OEHfPvtt7hy5QpWrlwJZ2dnzJkzB+fPn0deXh7q1auHyZMno23btmJdnTt3xqBBg3Djxg3s2bMHNjY2GDt2LAYPHgygKIE6b9487Nu3D5mZmXBwcMBrr72G0NBQAEU9gD/99FMcPHgQf//9NxwdHTFlyhT07NlT3EZcXBzmzJmD2NhYmJmZoXv37ggLC4OFhQWA/3qxNm3aFOvXr4dSqYSbmxtu376N8PBwhIeHi/U8LC8vDx9//DE6duyIJUuWiMs9PDzQvHlzZGVlicv+/vtvfPHFF7h8+TJq1aqFfv36YdKkSZDLi1Kle/bswZIlS3Djxg2YmZnB19cXS5cuxcqVKxERESHuLwCsWbMGrVu3ftKPs1xM2hIRERERERERET1HvvrqK0ydOhUeHh6wtrbGnTt30K5dO0yePBkmJibYunUrxowZgz179sDV1VV836pVqzBx4kSMGTMGe/fuxcyZM9GyZUvUq1cPa9euxcGDB7Fw4UK4uLjgzp07uHv3rs52v/32W3z44Yf43//+h23btuGDDz5AgwYN4OXlhdzcXIwaNQr+/v74/fffkZqaiunTp+Pzzz/HvHnzxDqio6NhaWmJVatWAShKUr/yyisYNGgQBg0aVO4+R0VFIT09HW+//XaZ662trQEA9+7dw+jRoxESEoL58+fj+vXrmD59OkxMTDBhwgTcv38fkydPxpQpU9C1a1c8ePAAMTExEAQBb731FuLj45GTkyMmkG1sbJ7sQ3oMJm2JiIiIiIiIiIieIxMnTkS7du3E1zY2NnjhhRdgbm4OiUSCSZMmYf/+/Th48CDeeOMNsVyHDh0wdOhQAMA777yD1atX48SJE6hXrx7u3LmDOnXq4MUXX4REIoGbm1up7fbs2RMDBw4EAEyaNAnHjh3D2rVrMXPmTOzYsQMqlQrz588Xh3CYMWMGxowZgw8//BAODg4AioZ3mD17ts6wCDKZDBYWFjq9jB+WmJgIAKhXr94jj80vv/wCZ2dnzJgxAxKJBF5eXrh37x4WLFiAcePGITk5GYWFhejWrZu4jyXHETY1NYVKpXpkLM8Ck7ZERERERERERETPkaZNm+q8fvDgARYuXIijR48iOTkZGo0G+fn5SEpK0ilXMjkpkUjg4OCA1NRUAEBISAjeeust9OzZE+3bt8dLL72EoKAgnff7+/vrvPbz88OlS5cAAPHx8fD29tYZczcgIABarRbXr18Xk7YNGzas0Di2xQRB0KtcfHw8/P39IZFIxGUvvvgicnNzcffuXfj4+CAwMBB9+/ZFUFAQgoKC0KNHj0rrUVseaZVujYiIiIiIiIiIiCqVmZmZzusvvvgChw4dwvvvv4/169dj69ataNiwIdRqtU654jFdi0kkEjEZ2rhxYxw4cADvvfce8vPzMWnSJEycOLHSY9eXp6cnACAhIeGpti+TybBq1Sr8+OOPqF+/PtauXYuePXvi5s2bT1VvRTFpS0RERERERERE9Bw7ffo0+vbti27dusHb2xsODg64fft2heuxtLREcHAwZs+ejW+++QZ79+5FRkaGuD42Nlan/D///AMvLy8AgJeXF+Li4pCbm6sTl1QqFROu5VEoFNBqtY8s065dO9ja2mLFihVlri+eiMzLywtnzpzR6Zl76tQpWFhYwNnZGUBRsvrFF1/ExIkTsXXrVigUCuzfv1/vWJ4FJm2JiIiIiIiIiIieY3Xr1sXBgwdx6dIlXL58GZMnT65w4nHVqlXYsWMH4uPjcf36dezZsweOjo7iBF8AsGfPHvz++++4fv06Fi1ahLNnz4pj5vbt2xdKpRJhYWG4cuUKjh8/js8//xyvvPKKODRCedzc3HDy5Encu3cPaWlpZZYpHgv38OHDGDNmDI4dO4Zbt27h3Llz+OKLL/Dpp58CAIYMGYK7d+/i888/R3x8PPbv34/FixfjzTffhFQqxT///INly5bh3LlzSEpKwr59+5CWliaOlevm5oa4uDgkJCQgLS2tVG/lZ4Vj2pJof/h+pMSnQCqXQiKRwNzOHC5NXNCodyOYWpuK5bRaLeL2xiEhKgE5yTlQmClg62EL32BfODdyfuQ2Yn+PxcUdF9F+Qnt4vOhR2btERERERERERPREapfIhVT37UydOhVhYWF4/fXXYWtri3feeQcPHjyoUB0WFhZYsWIFbty4AalUiqZNm+KHH36AVPpfn9AJEyZg165dmDVrFhwdHfHVV1+hfv36AIqGPVi5ciXmzJmDV199FWZmZujevTvCwsIeu+2JEydixowZ6Nq1K1QqFeLi4sos17VrV2zYsAE//PADJk+ejJycHLi4uKBNmzaYNGkSAKB27dr44Ycf8MUXX+DXX39FrVq18Oqrr2Ls2LEAinoTnzx5Ej///DNycnLg6uqKsLAwdOzYEQAwaNAg/P333xgwYAByc3OxZs0atG7dukLHUh8SQd9ReitBWFgYIiIiABSNmWFjYwNvb2/07t0b/fv31/nQAeDixYtYtmwZYmJikJ2dDRcXF7Rq1QqjRo16bDdqoGhGuk2bNuHjjz/GyJEj9YpRo9EgNjYWfn5+kMlkFd7HyiIIAv7N+Bdb4rcgS531TOrcH74f7gHu8OnhA0EQkJWUhXPbziHlWgp6fNoDZjZFY4pELY1C5q1MtBjeAg5eDoAEuHv+LpLOJqHl8Jbl1p/+bzqO/XAMqhwVWgxrwaQtVYwAqFSqosHIJY8vTlTtsc1TDWOrtEU7tDO6a66apKqvewvT0nB35kyobyc9vnANI3dzhf20aTBxdOT5QPQIxnq/TtVHfn4+rl+/Dk9PT5ia6iZOHxQUIl+tqbJYTBUyWJhUXt9KQRCQm5sLc3NznQm4niVvb28sWbIEXbt2rZT6q5NHtS19/3YZvKdt+/btER4eDq1Wi5SUFERGRmLOnDnYu3cvvv/+e3EA5EOHDmHChAkICgrCggUL4OHhgbS0NOzZswfffvstFi5c+Mjt/Pnnn/jnn3/g5ORUBXtV/UkkEti42aBtaFvsnrEbl/dchv9gf9y7fA+3Tt9C77m9YeVkJZZ383ODm59bufVptVqcWHUCLd5ogeM/Hn/ktrWFWpyNOIvE6ERo1BrU9q2NFm+0EHv7/jLyFwQMCcDVg1eRn5kPl6YuaDWyFZTmRTMLZt/Pxqn1p5CakAqZUob6HeujcZ/GkEglSIhMwOV9l+HRwgNX9l+BRCJBo96N4NPDBwCQlpiGk2tOIjMpEzK5DPZe9njp/Zee7mASERERERERUbViYSKv1CQq0eMYvPUplUo4OjoCKOqe3LhxYzRv3hwjR45EREQEBg4ciLy8PHz88cfo2LEjlixZIr7Xw8MDzZs3FwcSLs+9e/fw+eefY+XKlQgNDa3U/XneSGVSuAe44+6FuwCAO+fvwL6evU7CVh9xe+NQy70WavvUfmzZCzsv4PY/t9Htf92gtFDixE8ncGz5MXSe0lksk3gsEV2mdoFcKUfU0iic/uU02rzdBoUFhTg4/yC8u3uj/YT2yM/Mx19f/wUzGzN4dSwa+DozKROeSk+EfBOC5KvJOLjgINz83WDlZIWYdTFw83ND9+ndodVokZqQWqH9JCIiIiIiIiIielpGORFZYGAgfHx8sG/fPgBAVFQU0tPT8fbbb5dZvuSAxw/TarWYMmUKRo0ahQYNGjxxTIIgGN3Pf8E9o59y6jOvZY6CnAJAAAqyCmBWy6xC9ebcy8GV/VfgP8j/kdsp/rl+9Dqa9GkCCzsLKEwUCHgtAHcv3EVuWq74ft9evjCvZQ6luRLNQpoh8XgiBI2ApH+SoLRQwqe7D2QyGSzsLODdzRuJxxPF95pYmsC3py+kMilq+9SGhb0F0m+kA0JRkvpB6gPkpedBJpfBqaHTszu+/HmqH6HEf4aOhT/8qYoftnn+1MgfIiIiIqq24uLiODTCM2TwnrblqVevnjiocGJiorison788UfI5XIMHz78qeLJysoqNcauoWk0GqjUKqhUqmdSn1bQQlOoKVVfdko2FOYKqFQqyM3lyEjKqNA2j686jkYvN4JEKYFKpYIAAYWFheXWkZueC2UtpbhebiGHVC5F1v0syC2KmqzS5r/1SmsltIVaZKdlI/NuJjJuZeC3d38T6xMEAea25lCpVCgsLISJlYnOtmVKGfJz8qFSqRAwLAAXd1zE7pm7oTRXwquTF+p3qq/3vlLlKW43ACDhAJ9UA7DNU02jlqgBpaGjICIiIiIyDkabtBUEQRwY+UnnSjt//jzWrFmDLVu2PPUgy9bW1kY1sLkgCMjMzIRSoYRSeDZ3OFKJFDK5rGjSm/+n1Whx5+wduDZzhVKphHtzd1zdfxWqDBUsnSz1qvf+5fvIvJWJs7+dBQCoHqhwctVJpHVIw4uvv1iqvLmtOVQZKjGOvMw8aAu1sHayFpepMv9bn5WdBalcCis7K1g5WcGurh16fNKjzFjkcjkkEonOPkokEsjlciiVSti52SEoNAiCIBQNnfDlQTh7O8Ourp1e+0qVR/j/LlhKpZIJLKoR2OapplEoFIYOgYiIiIjIaBht0jY+Ph7u7u4AAE9PTwBAQkIC/P399a4jJiYGqamp6NSpk7hMo9Fg/vz5WLNmDQ4ePKh3XRKJpNJm13tqzzqs/68vMykT57efhzpPDZ+ePoAEqO1bG+4B7ji8+DBaDmsJ+3r2kEgkuHvxLm7H3kbL4S1LVdfv6346r/fN3oemrzSFRwuPMmP3bOuJCzsvwKGBA5QWSpzeeBrOjZ1hbmculrm05xIcGzpCrpTjXMQ51GldBxKZBG5+bvjn939w5eAVeLX3gkQuQc69HORl5KG2b4nxdMs6ZhIg4WgCXJq4wMzGDEoLJSRSCSRSCWduNwISQYLi//h5UE3ANk81Dts5ERERGYhWqzV0CPSceRZtyiiTttHR0bhy5QpGjhwJAGjXrh1sbW2xYsUKnYnIimVlZZU5ru0rr7yCtm3b6iwbNWoUXnnlFfTv379SYq/uYn+LxdmIs5BIJDCzNYNrU1f0nNkTptamYpm2Y9oibm8cTv58EjkpOVCYKWD7gi18e/mWWWfJZCtQlABXWiqhtCi7h3CjPo1QWFCIfbP3QavWwsnHCYGjA3XK1A2siwPzDyA/Mx/OjZ3x4pCiHrsKUwU6f9QZsZticX7beWjUGlg6WZYb28PuXriL2E2xKCwohKm1KfwH+cO2jq1e7yUiIiIiIiKi6kOpVEIqlSIpKQmOjo5FT7kZa4e9pyQIAgoKCiCVSp/bfTQGgiBApVIhOTkZUqlU50nvijJ40rZ4R7RaLVJSUhAZGYnly5ejU6dO6NevHwDA3Nwcs2fPxqRJkzBmzBgMHz4cL7zwAtLT07F7927cuXMH33zzTam6bW1tYWurm3BTKBRwcHB4ovFxn3ddP9ZvsGipVArfXr56J0If9spXrzxyvUwug/9gf/gPLr9XdW3v2vDp7lPmOisnK7Sf0L7MdfXa10O99rqfffDnweLvbUe3ffgtRERERERERPQckkql8PT0xJ07d5CUlGTocCqVIAhQq9VQKBRM2lYBc3NzvPDCC081P5bBk7aRkZEICgqCXC6HtbU1fHx8MH36dISEhOjsWNeuXbFhwwb88MMPmDx5MnJycuDi4oI2bdpg0qRJhtsBIiIiIiIiIiKqlpRKJV544QUUFhZCo9EYOpxKo9FocPnyZdSvX9+o5mx6HslkMnFOpadh0KTtvHnzMG/ePL3LN23aFIsXL36qbVZkHFsiIiIiIiIiInq+SSQSKBSK53pi1OKEtKmpKZO21YTBe9oSVdSQ1UMMHQIREREREREREVGlefKBFYiIiIiIiIiIiIjomWPSloiIiIiIiIiIiMiIMGlLREREREREREREZESYtCUiIiIiIiIiIiIyIkzaEhERERERERERERkRJm2JiIiIiIiIiIiIjAiTtkRERERERERERERGhElbIiIiIiIiIiIiIiPCpC0RERERERERERGREWHSloiIiIiIiIiIiMiIMGlLREREREREREREZESYtCUiIiIiIiIiIiIyIkzaEhERERERERERERkRJm2JiIiIiIiIiIiIjAiTtkRERERERERERERGhElbIiIiIiIiIiIiIiPCpC0RERERERERERGREWHSloiIiIiIiIiIiMiIMGlLREREREREREREZESYtCUiIiIiIiIiIiIyIkzaEhERERERERERERkRJm2JiIiIiIiIiIiIjIjc0AHQ07FSWgESQ0dBVAUEQC1RQ6FQsM1TzcA2TzWMpcISUBs6CiIiIiIi48CkbTVmobBAL89ekEh4N0/PP0EQUFhYCLlczjZPNQLbPNU0Wo0W967dM3QYRERERERGgUnbakyhUcDe2p4381QjCIKArKwsWFtas81TjcA2TzWNRqPBPTBpS0REREQEcEzbak0QBEOHQFSl2OappmGbJyIiIiIiqpmYtCUiIiIiIiIiIiIyIkzaEhERERERERERERkRJm2JiIiIiIiIiIiIjAiTtkRERERERERERERGhElbIiIiIiIiIiIiIiPCpC0RERERERERERGREWHSloiIiIiIiIiIiMiIMGlLREREREREREREZESYtCUiIiIiIiIiIiIyIkzaEhERERERERERERkRJm2JiIiIiB7jhx9+gLe3N+bMmSMuKygowKxZs9C6dWv4+/tjwoQJSElJ0XlfUlISRo8ejebNmyMwMBDz589HYWGhTpkTJ04gJCQETZo0Qbdu3bBly5ZS21+/fj06d+6Mpk2bYuDAgTh79qzOen1iISIiIqLqg0nbakwikRg6BKIqxTZPNQ3bPJFxOHv2LDZu3Ahvb2+d5XPnzsWhQ4ewcOFCrF27Fvfv38f48ePF9RqNBqGhoVCr1di4cSPmzZuHiIgILFq0SCxz8+ZNhIaGonXr1ti2bRtGjBiB6dOnIzIyUiyza9cuhIeHY9y4cYiIiICPjw9GjRqF1NRUvWMhIiIioupFbugA6MmpZWok5STxpp5qBEEQUKgtRE5ODts81Qhs81TTaDVaQ4dQpgcPHmDKlCmYPXs2vv/+e3F5dnY2Nm/ejAULFiAwMBBAUeI0ODgYsbGx8PPzQ1RUFK5du4ZVq1bBwcEBvr6+eO+997BgwQKMHz8eSqUSGzduhLu7O8LCwgAAXl5eOHXqFFavXo327dsDAFatWoVBgwZhwIABAIBZs2bhr7/+wubNmzF69Gi9YiEiIiKi6oVJ22rsgfoB/rz5J7LV2YYOhajyCYBarYZCoQCYv6KagG2eahgbhQ2ao7mhwyjls88+Q8eOHdG2bVudpO358+ehVqvRtm1bcZmXlxdcXV3FRGlsbCwaNmwIBwcHsUxQUBBmzpyJa9euoVGjRoiNjRUTrSXLzJ07FwCgUqlw4cIFhIaGiuulUinatm2LM2fO6B1LRQiCAEEQKvQeesb+//DzsyB6tOLzg+cK0ePxfDEe+h5/Jm2ruWxVNrLUWYYOg6jyCUU3rkpByQQW1Qxs81TDyCAzdAil7Ny5ExcvXsTvv/9eal1KSgoUCgWsra11ltvb2yM5OVksUzJhC0B8/bgyOTk5yM/PR2ZmJjQaDezt7UttJyEhQe9YKiIrKwtSaeWOoiaVSmGq1UKj0UKj0VTqtqojibbomOTk5Bg4EiLjptUWPaVRFX+3iKo7ni/Go/izeBwmbYmIiIiIHnLnzh3MmTMHP/30E0xMTAwdTpWytraGTFb5SXRNejpkMim0VbCt6kYmLTomlpaWVfJZEFVXxV/6VNXfLaLqjOeL8dD3C2smbYmIiIiIHnLhwgWkpqaif//+4jKNRoOTJ09i/fr1WLlyJdRqNbKysnR6uKampsLR0RFAUY/Zs2fP6tSbkpICADplipeVLGNpaQlTU1NIpVLIZDKdSceKt1PcQ9fBweGxsVSERCLhWNqG9v+Hn58F0aMVnx88V4gej+eL8dD3+LM/NBERERHRQ9q0aYM//vgDW7duFX+aNGmCvn37ir8rFApER0eL70lISEBSUpI4hqyfnx+uXLmik3A9duwYLC0tUb9+fbHM8ePHdbZ97NgxsQ6lUonGjRvrbEer1SI6Ohr+/v4AoFcsRERERFS9sKctEREREdFDLC0t0bBhQ51l5ubmqFWrlrh8wIABmDdvHmxsbGBpaYnZs2fD399fTJQGBQWhfv36+OijjzBlyhQkJydj4cKFGDp0KJRKJQDgtddew/r16/HFF19gwIABOH78OHbv3o3ly5eL233zzTcxdepUNGnSBM2aNcPPP/+MvLw8sRewlZXVY2MhIiIiouqFSVsiIiIioicwbdo0SKVSTJw4ESqVCkFBQfj000/F9TKZDMuWLcPMmTMxePBgmJmZISQkBBMnThTLeHh4YPny5QgPD8eaNWvg7OyM2bNno3379mKZ4OBgpKWlYdGiRUhOToavry9WrFihM4HZ42IhIiIioupFIgiCYOggjJlGo0FsbCz8/PyMaqBmQRDwb8a/2BK/BVnqLEOHQ1T5BEClUhX1TOLwO1QTsM1TDWOrtEU7tDO6a66apKqvewvT0nB35kyobydV+raqG7mbK+ynTYOJoyPPB6JHMNb7dSJjxPPFeOj7WXBMWyIiIiIiIiIiIiIjwqQtERERERERERERkRFh0paIiIiIiIiIiIjIiDBpS0RERERERERERGREmLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiIiIiIiIiIjwqQtERERERERERERkRFh0paIiIiIiIiIiIjIiDBpS0RERERERERERGREmLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERkRu6ADIeOwP34+U+BRI5VJIJBKY25nDpYkLGvVuBFNrU7GcVqtF3N44JEQlICc5BwozBWw9bOEb7AvnRs6l6k06m4Qzv55BblouJBIJ7OraIeC1ANTyqFWFe0dERERERERERFQ9GDRpGxYWhoiIiKJA5HLY2NjA29sbvXv3Rv/+/SGV6nYEvnjxIpYtW4aYmBhkZ2fDxcUFrVq1wqhRo+Dp6VnmNhYvXoydO3fi7t27UCgUaNy4Md5//300b9680vevOvIb6AefHj4QBAFZSVk4t+0c9szcgx6f9oCZjRkA4NiyY8i8lYkWw1vAwcsBkAB3z9/FzZibZSZtbV+wRecPO8Oslhm0Gi2u7L+CI4uP4OUvXq7q3SMiIiIiIiIiIjJ6Bu9p2759e4SHh0Or1SIlJQWRkZGYM2cO9u7di++//x5yeVGIhw4dwoQJExAUFIQFCxbAw8MDaWlp2LNnD7799lssXLiwzPrr1q2LGTNmwMPDA/n5+Vi9ejXeeust/Pnnn7Czs6vCPa1eJBIJbNxs0Da0LXbP2I3Ley7Df7A/7l2+h1unb6H33N6wcrISy7v5ucHNz63Musxqmf33QgAkUgkepDyAtlALqbz0CB3aQi3ORpxFYnQiNGoNavvWRos3Woi9fX8Z+QsChgTg6sGryM/Mh0tTF7Qa2QpKcyUAIPt+Nk6tP4XUhFTIlDLU71gfjfs0hkQqQUJkAi7vuwyPFh64sv8KJBIJGvVuBJ8ePgCAtMQ0nFxzEplJmZDJZbD3ssdL77/0bA4qERERERERERGRHgyetFUqlXB0dAQA1K5dG40bN0bz5s0xcuRIREREYODAgcjLy8PHH3+Mjh07YsmSJeJ7PTw80Lx5c2RlZZVbf9++fXVef/zxx/j9998RFxeHwMDAytmp54hUJoV7gDvuXrgLALhz/g7s69nrJGz18SD1AXZN34XC/EIIENC4T+MyE7YAcGHnBdz+5za6/a8blBZKnPjpBI4tP4bOUzqLZRKPJaLL1C6QK+WIWhqF07+cRpu326CwoBAH5x+Ed3dvtJ/QHvmZ+fjr679gZmMGr45eAIDMpEx4Kj0R8k0Ikq8m4+CCg3Dzd4OVkxVi1sXAzc8N3ad3h1ajRWpC6hMeOSIiIiIiIiIioidjlBORBQYGwsfHB/v27QMAREVFIT09HW+//XaZ5a2trfWqV6VSYdOmTbCysoK3t/czi/d5Z25rjoIHBQCAgqwC3Z6zerKwt8DA7wfi1aWv4sWhL8Le077cstePXUeTvk1gYW8BhakCAa8H4O6Fu8hNzxXL+Ab7wtzWHEoLJZr1b4bE44kQtAKS/kmC0kIJnx4+kMllsLC3gHc3byQeTxTfa2JpAt9evpDKpajtWxsWDhZIv5EOoChJ/SD1AfIy8iBTyODk7VThfSUiIiIiIiIiInoaBu9pW5569eohLi4OAJCYmCguexKHDh3CBx98gLy8PDg6OuKnn36q8NAIgiBAEIQn2n5l0InlWYf1UH25abkwsTABhKKEZ9adrCfepsJUgYadGmLzxM3o6d4Tlo6WpcrkpeXBwt5C3IZ5LXNI5VLkpuXCvJY5AMDC7r/1FvYW0BZqkZ+Vj5yUHGTczsBvY3/7b3cEQae8qbWpTvxypRyF+YWAALR+q7U4jq/SXIkGXRrAuysT/MZAKPGfRJAYOhyiSsc2TzWOAIBNnYiIiIgIgBEnbQVBgEQiEX9/Gq1bt8bWrVuRnp6OX3/9FZMmTcJvv/0Ge/vye3s+LCsrq9TEaIam0WigUqugUqmeSX1aQQtNoUanPq1Gi5unb8K5iTNUKhUcfBxwed9lpN1OKzPhqg9BK0Cj0iDjbgaUNspS681szZB5NxPWHkU9qPMz86Et1EJhqRBjy7z33/rMu5mQyqWQmkhhYm1SNPFZWOdS9apUKhQWFkIQBJ19FAQBhYWFUKlUMKllghYjWkAQBKTGp+LIN0dQq04t2NaxfaJ9pWdHQNHnBAAS3tVTDcA2TzWNWqIGSl8WEBERERHVSEabtI2Pj4e7uzsAwNPTEwCQkJAAf3//Ctdlbm6OOnXqoE6dOvDz80P37t3x+++/IzQ0VO86rK2tIZPJKrztyiIIAjIzM6FUKKEUns0djlQihUwug1JZVF/mnUyc334ehfmFaBzcGEqlEu5N3eEe4I7o76PRcnhL2HvaQyKR4O6lu7gdexsth7UsVW/iiUTY17WHpaMl1PlqnN18FnITOZy8nMRtleQZ6Im4vXFw9nGG0kKJc5vPwbmRM2ycbMQyV/+8ChdfF8hN5Li04xLqtKoDE1MTvPDiCzi/9TwSoxLh1d4LEpkEOfdykJeZh9o+tSGXyyGRSHS2K5FIIJfLoVQqkXA0AS5NXGBmYwZza3NIpBIoTZRlxklVS/j/7tFKpZIJLKoR2OapplEoFIYOgYiIiIjIaBhl0jY6OhpXrlzByJEjAQDt2rWDra0tVqxYoTMRWbGsrCy9x7UFAK1WW+HeqRKJROz5a3SeYVixv8XibMRZSCQSmNmawbWpK3rO7Fk0pMD/azumLeL2xuHkzyeRk5IDhZkCti/YwreXb5mxPEh5gH9+/wf5WfmQm8hhX88enad0htKi7ERoo76NUKgqxL45+6BVa+Hk44TA0ECduuu2rYsDXxxAfmY+nBs748WhLwISQGGmQOePOiN2UyzObzsPjVoDSyfL0rGVdcwkwN2LdxH7aywKCwpham0K/0H+7GVrJCSCBMX/MX9FNQHbPNU4bOdERERERCKDJ21VKhWSk5Oh1WqRkpKCyMhILF++HJ06dUK/fv0AFPWUnT17NiZNmoQxY8Zg+PDheOGFF5Ceno7du3fjzp07+Oabb0rVnZubi2XLlqFz585wdHREeno61q9fj3v37qFnz55VvKfGr+vHXfUqJ5VK4dvLtygRqofGfRqjcZ/Geschk8vgP9gf/oPL71Vd27s2fLr7lLnOyskK7Se0L3Ndvfb1UK+97tjIwZ8Hi7+3Hd1W7ziJiIiIiIiIiIgqg8GTtpGRkQgKCoJcLoe1tTV8fHwwffp0hISE6Iwh27VrV2zYsAE//PADJk+ejJycHLi4uKBNmzaYNGlSmXXLZDIkJCQgIiIC6enpqFWrFpo2bYr169ejQYMGVbSHRERERERERERERPozaNJ23rx5mDdvnt7lmzZtisWLF+td3sTEBN99992ThEZERERERERERERkEAbvaUtUUUNWDzF0CERERERERERERJVG+vgiRERERERERERERFRVmLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiIiIiIiIiIjwqQtERERERERERERkRFh0paIiIiIiIiIiIjIiDBpS0RERERERERERGREmLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiIiIiIiIiIjwqQtERERERERERERkRFh0paIiIiIiIiIiIjIiDBpS0RERERERERERGREmLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiIiIiIiIiIjIjd0APR0rJRWgMTQURBVAQFQS9RQKBRs81QzsM1TDWOpsATUho6CiIiIiMg4MGlbjVkoLNDLsxckEt7N0/NPEAQUFhZCLpezzVONwDZPNY1Wo8W9a/cMHQYRERERkVFg0rYaU2gUsLe258081QiCICArKwvWltZs81QjsM1TTaPRaHAPTNoSEREREQEc07ZaEwTB0CEQVSm2eapp2OaJiIiIiIhqJiZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiIiIiIiIiIjwqQtERERERERERERkRFh0paIiIiIiIiIiIjIiDBpS0RERERERERERGREmLQlIiIiIiIiIiIiMiJM2hIREREREREREREZESZtiYiIiIiIiIiIiIwIk7ZERERERERERERERoRJWyIiIiKiMvzyyy/o27cvAgICEBAQgMGDB+Pw4cPi+oKCAsyaNQutW7eGv78/JkyYgJSUFJ06kpKSMHr0aDRv3hyBgYGYP38+CgsLdcqcOHECISEhaNKkCbp164YtW7aUimX9+vXo3LkzmjZtioEDB+Ls2bM66/WJhYiIiIiqDyZtqzGJRGLoEIiqFNs81TRs80SG5ezsjA8//BBbtmzB5s2b0aZNG4wbNw5Xr14FAMydOxeHDh3CwoULsXbtWty/fx/jx48X36/RaBAaGgq1Wo2NGzdi3rx5iIiIwKJFi8QyN2/eRGhoKFq3bo1t27ZhxIgRmD59OiIjI8Uyu3btQnh4OMaNG4eIiAj4+Phg1KhRSE1NFcs8LhYiIiIiql7khg6AnpxapkZSThJv6qlGEAQBhdpC5OTksM1TjcA2TzWNVqM1dAildO7cWef1+++/jw0bNiA2NhbOzs7YvHkzFixYgMDAQABFidPg4GDExsbCz88PUVFRuHbtGlatWgUHBwf4+vrivffew4IFCzB+/HgolUps3LgR7u7uCAsLAwB4eXnh1KlTWL16Ndq3bw8AWLVqFQYNGoQBAwYAAGbNmoW//voLmzdvxujRo5Gdnf3YWIiIiIioemHSthp7oH6AP2/+iWx1tqFDIap8AqBWq6FQKADmr6gmYJunGsZGYYPmaG7oMMql0WiwZ88e5Obmwt/fH+fPn4darUbbtm3FMl5eXnB1dRUTpbGxsWjYsCEcHBzEMkFBQZg5cyauXbuGRo0aITY2Vky0liwzd+5cAIBKpcKFCxcQGhoqrpdKpWjbti3OnDkDAHrFQkRERETVC5O21Vy2KhtZ6ixDh0FU+YSiG1eloGQCi2oGtnmqYWSQGTqEMsXFxeG1115DQUEBzM3NsWTJEtSvXx+XLl2CQqGAtbW1Tnl7e3skJycDAFJSUnQStgDE148rk5OTg/z8fGRmZkKj0cDe3r7UdhISEsQ6HhdLRQiCAEEQKvw+eob+//DzsyB6tOLzg+cK0ePxfDEe+h5/Jm2JiIiIiMrh6emJrVu3Ijs7G3v37sXUqVOxbt06Q4dVqbKysiCVVu7UF1KpFKZaLTQaLTQaTaVuqzqSaIuOSU5OjoEjITJuWm3R0DpV8XeLqLrj+WI8ij+Lx2HSloiIiIioHEqlEnXq1AEANGnSBOfOncOaNWvQq1cvqNVqZGVl6fRwTU1NhaOjI4CiHrNnz57VqS8lJQUAdMoULytZxtLSEqamppBKpZDJZDqTjhVvp7iHroODw2NjqQhra2vIZJXf81mTng6ZTAptFWyrupFJi46JpaVllXwWRNVV8Zc+VfV3i6g64/liPPT9wppJWyIiIiIiPWm1WqhUKjRp0gQKhQLR0dHo0aMHACAhIQFJSUniGLJ+fn5YtmwZUlNTxeENjh07BktLS9SvX18sc+TIEZ1tHDt2TKxDqVSicePGiI6ORteuXcUYoqOj8cYbbwCAXrFUhEQi4QSIhvb/h5+fBdGjFZ8fPFeIHo/ni/HQ9/gzaUtEREREz4WsrCz8+eefiImJQVJSEvLz82Fra4tGjRohKCgIAQEBFarvq6++QocOHeDi4oIHDx5gx44d+Pvvv7Fy5UpYWVlhwIABmDdvHmxsbGBpaYnZs2fD399fTJQGBQWhfv36+OijjzBlyhQkJydj4cKFGDp0KJRKJQDgtddew/r16/HFF19gwIABOH78OHbv3o3ly5eLcbz55puYOnUqmjRpgmbNmuHnn39GXl4e+vfvDwB6xUJERERE1QuTtkRERERUrd27dw+LFi3CH3/8AScnJzRr1gy+vr4wMTFBZmYmTpw4gZ9++gmurq4YP348goOD9ao3NTUVU6dOxf3792FlZQVvb2+sXLkS7dq1AwBMmzYNUqkUEydOhEqlQlBQED799FPx/TKZDMuWLcPMmTMxePBgmJmZISQkBBMnThTLeHh4YPny5QgPD8eaNWvg7OyM2bNno3379mKZ4OBgpKWlYdGiRUhOToavry9WrFihM4HZ42IhIiIioupFInDKuEfSaDSIjY2Fn5+fUY35IQgC/s34F1vityBLnWXocIgqnwCoVKqinkl8koNqArZ5qmFslbZoh3ZPdM3Vtm1b9OvXD/379xeHHXhYfn4+9u/fj7Vr16J79+4YNWrUswj7uVLV172FaWm4O3Mm1LeTKn1b1Y3czRX206bBxNHRqO5BiIyNsd6vExkjni/GQ9/Pgj1tiYiIiKha27lzJ2xtbR9ZxtTUFH369EGfPn2Qnp5eRZERERERET0ZqaEDICIiIiJ6Go9L2D5teSIiIiKiqsakLRERERE9t+Lj47F582ZcunTJ0KEQEREREemNwyMQERER0XPhu+++g6mpKd5++20AwPHjx/H222/DwsICOTk5CA8Px8svv2zgKImIiIiIHo89bYmIiIjoubB3716diciWLVuGYcOG4cSJE/jkk0+wfPlyA0ZHRERERKQ/9rQlIiIiompt69atEAQBt2/fxqVLl5CRkQFBEHD69Gm0aNECW7duhVarxc2bN7F161YAQL9+/QwaMxERERHRozBpS0RERETVmqurKwBAoVDAwcEBrq6uuHTpEiwtLdG6dWsIggCVSgWJRAI3NzcIgmDgiImIiIiIHo1JWyIiIiKq1lq1agUAaNy4MQ4dOoR33nkHkZGR6NixI1q2bAkAiIuLg7Ozs/iaiIiIiMiYcUxbIiIiInoufPTRR7h8+TJef/11JCUlYeLEieK6iIgItG/f3oDRERERERHpjz1tiYiIiOi54OPjg4MHDyI9PR22trY669566y1YWloaKDIiIiIiooph0paIiIiInisPJ2wBwMnJyQCREBERERE9GQ6PQERERETV2s6dO/Uue+fOHZw6daoSoyEiIiIienpM2hIRERFRtbZhwwb06tULP/74I+Lj40utz87OxuHDhzF58mSEhIQgIyOj6oMkIiIiIqoADo9ARERERNXaunXrcODAAaxbtw5ff/01zMzM4ODgABMTE2RmZiIlJQW2trYICQnBjh074ODgYOiQiYiIiIgeiUlbIiIiIqr2unTpgi5duiAtLQ2nT5/G7du3UVBQAFtbW/j6+qJRo0aQSvmQGRERERFVDxVK2iYmJmLRokX47LPPSs2+m52djZkzZ2LSpEnw8PB4pkESEREREenDzs4OXbt2NXQYRERERERPpULdDVauXAlnZ+dSCVsAsLKygrOzM1asWPHMgiMiIiIiIiIiIiKqaSrU0/bvv//Gl19+We76Xr16YfLkyU8dFBnG/vD9SIlPgVQuhUQigbmdOVyauKBR70YwtTYVy2m1WsTtjUNCVAJyknOgMFPA1sMWvsG+cG7kXKrehKgEXDlwBdl3syFTyuDazBUBrwVAaaGsyt0jIiIiIiIiIiKqFiqUtL1z5w7s7e3LXW9ra4u7d+/qXV9YWBgiIiKKApHLYWNjA29vb/Tu3Rv9+/cvNe7YxYsXsWzZMsTExCA7OxsuLi5o1aoVRo0aBU9Pz1L1q9VqLFy4EEeOHMHNmzdhaWmJtm3bYvLkyahdu7becdYkfgP94NPDB4IgICspC+e2ncOemXvQ49MeMLMxAwAcW3YMmbcy0WJ4Czh4OQAS4O75u7gZc7PMpK1GpYH/IH84eDmgUFWIYz8cw8m1J9FuTLuq3j0iIiIiIiIiIiKjV6GkrZWVFf7991+4ubmVuf7ff/8tc+iER2nfvj3Cw8Oh1WqRkpKCyMhIzJkzB3v37sX3338PubwoxEOHDmHChAkICgrCggUL4OHhgbS0NOzZswfffvstFi5cWKru/Px8XLx4EWPHjoWPjw+ysrIwZ84cjB07Flu2bKlQnDWNRCKBjZsN2oa2xe4Zu3F5z2X4D/bHvcv3cOv0LfSe2xtWTlZieTc/N7j5ld0uGnRuIP4uU8rQoFMDxKyNKXfb2kItzkacRWJ0IjRqDWr71kaLN1qIvX1/GfkLAoYE4OrBq8jPzIdLUxe0GtkKSvOinrvZ97Nxav0ppCakQqaUoX7H+mjcpzEkUgkSIhNwed9leLTwwJX9VyCRSNCodyP49PABAKQlpuHkmpPITMqETC6DvZc9Xnr/pac8mkRERERERERERPqrUNK2RYsWWLduHQIDA8tcv2bNGrz44osVCkCpVMLR0REAULt2bTRu3BjNmzfHyJEjERERgYEDByIvLw8ff/wxOnbsiCVLlojv9fDwQPPmzZGVlVVm3VZWVli1apXOsk8++QQDBw5EUlISXF1dKxRrTSSVSeEe4I67F4p6UN85fwf29ex1ErYVdf/yfdTyqFXu+gs7L+D2P7fR7X/doLRQ4sRPJ3Bs+TF0ntJZLJN4LBFdpnaBXClH1NIonP7lNNq83QaFBYU4OP8gvLt7o/2E9sjPzMdfX/8FMxszeHX0AgBkJmXCU+mJkG9CkHw1GQcXHISbvxusnKwQsy4Gbn5u6D69O7QaLVITUp94P4mIiKjqfffddxg1ahTMzMx0lufn52PFihUYP368gSIjIiIiItJfhSYiCw0NxZEjRzBx4kScPXsW2dnZyM7Oxj///IMJEyYgKioKoaGhTx1UYGAgfHx8sG/fPgBAVFQU0tPT8fbbb5dZ3traWu+6c3JyIJFIKvQeABAEweh+/gvuGf2UU595LXMU5BQAAlCQVQCzWmZPvI2kf5IQfyQefgP8yi1z/eh1NOnTBBZ2FlCYKBDwWgDuXriL3LRcMU7fXr4wr2UOpbkSzUKaIfF4IgSNgKR/kqC0UMKnuw9kMhks7Czg3c0biccTxfeaWJrAt6cvpDIpavvUhoW9BdJvpANCUZL6QeoD5KXnQSaXwamh07M7vvx5qh+hxH+GjoU//KmKH7Z5/tTIn2dgyZIlyM3NLbU8Ly9P58t/IiIiIiJjVqGeto0aNcKiRYswbdo0/Pnnn+JyQRBga2uLhQsXonHjxs8ksHr16iEuLg4AkJiYKC57GgUFBViwYAF69+5d4WEcsrKySo2xa2gajQYqtQoqleqZ1KcVtNAUakrVl52SDYW5AiqVCnJzOTKSMp5om/cv38fxH46jTWgbmNc2L7eO3PRcKGspxfVyCzmkcimy7mdBblHUZJU2/61XWiuhLdQiOy0bmXczkXErA7+9+5tYnyAIMLct2l5hYSFMrEx0ti1TypCfkw+VSoWAYQG4uOMids/cDaW5El6dvFC/U/0K7ys9ewIEFBYWAgAkkBg4GqLKxzZPNY1aogaewRylgiBAIil9zly+fBk2NjZPvwEiIiIioipQoaQtAHTq1AmHDh1CVFQUEhMTIQgCPD090a5du1KPoT2NkhfcOr1Kn5BarcZ7770HQRAwa9asCr/f2toaMpnsqeN4VgRBQGZmJpQKJZTCM7jDASCVSCGTy6BU/lefVqPFnbN34NrMFUqlEu7N3XF1/1WoMlSwdNI/8X334l0c/+E42oa2hWvTRw9LYW5rDlWGSowjLzMP2kItrJ2sxWWqzP/WZ2VnQSqXwsrOClZOVrCra4cen/Qos265XA6JRKKzjxKJBHK5HEqlEnZudggKDYIgCEVDJ3x5EM7ezrCra6f3vlLlEP6/C5ZSqWQCi2oEtnmqaRQKxVO9v2XLlpBIJJBIJOjRo4dO4laj0SA3Nxevvfba04ZJRERERFQlKpy01Wq12LFjB/7880/cvn0bEokEbm5uyMnJwSuvvFJmz4YnER8fD3d3dwCAp6cnACAhIQH+/v4VrkutVmPSpElISkrCzz//XOFetgDEmwCj9KzD+v/6MpMycX77eajz1PDp6QNIgNq+teEe4I7Diw+j5bCWsK9nD4lEgrsX7+J27G20HN6yVHX3Lt1D5JJItB3dFq7NHj+OsGdbT1zYeQEODRygtFDi9MbTcG7sDHM7c7HMpT2X4NjQEXKlHOcizqFO6zqQyCRw83PDP7//gysHr8CrvRckcgly7uUgLyMPtX1rl9rHh/c74WgCXJq4wMzGDEoLJSRSCSRSybM/xlRhEkGC4v/4eVBNwDZPNc5TtvNp06ZBEARMmzYNEyZMgJXVf+PvKxQKuLm5PdF1JBERERGRIVQoaSsIAsaOHYvDhw/Dx8cHDRs2hCAIiI+PR1hYGPbt24elS5c+dVDR0dG4cuUKRo4cCQBo164dbG1tsWLFijLHIsvKyip3jNrihO2NGzewZs0a2NraPnV8z7PY32JxNuIsJBIJzGzN4NrUFT1n9oSptalYpu2YtojbG4eTP59ETkoOFGYK2L5gC99evmXWeW7bORTmFeLo90d1lg9aPqjM8o36NEJhQSH2zd4HrVoLJx8nBI7WnfyubmBdHJh/APmZ+XBu7IwXhxRNgKcwVaDzR50RuykW57edh0atgaWTZbmxPezuhbuI3RSLwoJCmFqbwn+QP2zrsM0QEREZu5CQEACAu7s7AgICIJdXuG8CEREREZHRqNDV7JYtW3Dy5EmsXr0abdq00VkXHR2NcePGYevWrejXr5/edapUKiQnJ0Or1SIlJQWRkZFYvnw5OnXqJNZjbm6O2bNnY9KkSRgzZgyGDx+OF154Aenp6di9ezfu3LmDb775plTdarUaEydOxMWLF7F8+XJoNBokJycDAGxsbHQekSeg68dd9SonlUrh28tX70Ro1zD96i0mk8vgP9gf/oPL7w1T27s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+            "text/plain": [
+              "<Figure size 1400x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# -----------------------------\n",
+        "# Visualize multi-period solution\n",
+        "# -----------------------------\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+        "\n",
+        "# Plot 1: Opening schedule timeline\n",
+        "ax1 = axes[0]\n",
+        "for i in I:\n",
+        "    if i in dc_open_period:\n",
+        "        t_open = dc_open_period[i]\n",
+        "        ax1.barh(i, num_periods - t_open, left=t_open, alpha=0.6, color='tab:green')\n",
+        "        ax1.text(t_open + 0.1, i, f'DC {i} opens', va='center', fontsize=9)\n",
+        "    else:\n",
+        "        ax1.barh(i, 0.1, left=0, alpha=0.3, color='gray')\n",
+        "        ax1.text(0.15, i, f'DC {i} (never opened)', va='center', fontsize=9, color='gray')\n",
+        "\n",
+        "ax1.set_xlabel('Period')\n",
+        "ax1.set_ylabel('DC')\n",
+        "ax1.set_title('DC Opening Schedule', fontsize=14, fontweight='bold')\n",
+        "ax1.set_yticks(I)\n",
+        "ax1.set_yticklabels([f'DC {i}' for i in I])\n",
+        "ax1.set_xticks(T)\n",
+        "ax1.set_xlim(-0.2, num_periods)\n",
+        "ax1.grid(True, alpha=0.3)\n",
+        "\n",
+        "# Plot 2: Costs by period\n",
+        "ax2 = axes[1]\n",
+        "periods = summary_df['Period']\n",
+        "width = 0.35\n",
+        "x = np.arange(len(periods))\n",
+        "\n",
+        "ax2.bar(x - width/2, summary_df['Opening Cost ($)'], width, label='Opening Cost', alpha=0.8, color='tab:red')\n",
+        "ax2.bar(x + width/2, summary_df['Transport Cost ($)'], width, label='Transport Cost', alpha=0.8, color='tab:blue')\n",
+        "\n",
+        "ax2.set_xlabel('Period')\n",
+        "ax2.set_ylabel('Cost ($)')\n",
+        "ax2.set_title('Costs by Period (Undiscounted)', fontsize=14, fontweight='bold')\n",
+        "ax2.set_xticks(x)\n",
+        "ax2.set_xticklabels([f'Period {t}' for t in periods])\n",
+        "ax2.legend()\n",
+        "ax2.grid(True, alpha=0.3, axis='y')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Summary and Business Insights\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "======================================================================\n",
+            "                    BUSINESS INSIGHTS\n",
+            "======================================================================\n",
+            "\n",
+            "📊 Single-Period MILP Results:\n",
+            "   • Optimal Cost: $441,274.54\n",
+            "   • DCs Opened: 4 of 5\n",
+            "   • Solve Time: 0.481 seconds\n",
+            "\n",
+            "📈 Multi-Period Results:\n",
+            "   • Total Discounted Cost: $633,493.66\n",
+            "   • Planning Horizon: 3 periods\n",
+            "   • DCs Eventually Opened: 5 of 5\n",
+            "   • Solve Time: 2.633 seconds\n",
+            "\n",
+            "🔄 Semi-Relaxation Insights:\n",
+            "   • Semi-Relaxed Objective: $440,597.95\n",
+            "   • Integrality Gap: $676.59 (0.15%)\n",
+            "\n",
+            "⚡ Performance:\n",
+            "   • GPU Acceleration: Enabled ✓\n",
+            "   • Single-period problem: 105 variables, 25 constraints\n",
+            "   • Multi-period problem: 330 variables, 115 constraints\n",
+            "\n",
+            "======================================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\n\" + \"=\"*70)\n",
+        "print(\" \" * 20 + \"BUSINESS INSIGHTS\")\n",
+        "print(\"=\"*70 + \"\\n\")\n",
+        "\n",
+        "print(\"📊 Single-Period MILP Results:\")\n",
+        "print(f\"   • Optimal Cost: ${prob.ObjValue:,.2f}\")\n",
+        "print(f\"   • DCs Opened: {len(open_dcs)} of {num_dcs}\")\n",
+        "print(f\"   • Solve Time: {solve_time:.3f} seconds\")\n",
+        "print()\n",
+        "\n",
+        "print(\"📈 Multi-Period Results:\")\n",
+        "print(f\"   • Total Discounted Cost: ${prob_mp.ObjValue:,.2f}\")\n",
+        "print(f\"   • Planning Horizon: {num_periods} periods\")\n",
+        "print(f\"   • DCs Eventually Opened: {len(dc_open_period)} of {num_dcs}\")\n",
+        "print(f\"   • Solve Time: {solve_time_mp:.3f} seconds\")\n",
+        "print()\n",
+        "\n",
+        "print(\"🔄 Semi-Relaxation Insights:\")\n",
+        "if prob_lp.Status in (1, 2) and prob.Status in (1, 2):\n",
+        "    gap = prob.ObjValue - prob_lp.ObjValue\n",
+        "    print(f\"   • Semi-Relaxed Objective: ${prob_lp.ObjValue:,.2f}\")\n",
+        "    if prob.ObjValue > 0:\n",
+        "        print(f\"   • Integrality Gap: ${gap:,.2f} ({gap/prob.ObjValue*100:.2f}%)\")\n",
+        "    else:\n",
+        "        print(f\"   • Integrality Gap: ${gap:,.2f}\")\n",
+        "else:\n",
+        "    print(\"   • Solution not available\")\n",
+        "print()\n",
+        "\n",
+        "print(\"⚡ Performance:\")\n",
+        "print(f\"   • GPU Acceleration: Enabled ✓\")\n",
+        "print(f\"   • Single-period problem: {prob.NumVariables} variables, {prob.NumConstraints} constraints\")\n",
+        "print(f\"   • Multi-period problem: {prob_mp.NumVariables} variables, {prob_mp.NumConstraints} constraints\")\n",
+        "print(\"\\n\" + \"=\"*70)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "This notebook demonstrated how to use the cuOpt linear programming API to solve a Capacitated Facility Location Problem (CFLP).\n",
+        "\n",
+        "### What We Achieved:\n",
+        "1. **Optimal DC selection** to minimize total logistics cost\n",
+        "2. **Customer-to-DC assignment** respecting capacity constraints\n",
+        "3. **LP relaxation** for lower bound analysis\n",
+        "4. **Multi-period extension** for capacity expansion planning\n",
+        "\n",
+        "### Key Advantages of cuOpt:\n",
+        "- **GPU Acceleration**: Significantly faster than CPU-based solvers\n",
+        "- **Scalability**: Handles problems with thousands of variables and constraints\n",
+        "- **Integration**: Easy to integrate into existing Python workflows\n",
+        "- **Real-time**: Fast enough for operational decision-making\n",
+        "\n",
+        "### Possible Extensions:\n",
+        "\n",
+        "**High Priority - Service Levels:**\n",
+        "- Maximum distance constraints (customers must be within X km of assigned DC)\n",
+        "- Multiple service tiers with different SLAs\n",
+        "- Backup DC assignments for redundancy\n",
+        "\n",
+        "**Additional Enhancements:**\n",
+        "- Multiple product types with different handling requirements\n",
+        "- DC capacity expansion options (modular capacity)\n",
+        "- Stochastic demand scenarios\n",
+        "- Multi-echelon supply chain (plants → DCs → customers)\n",
+        "- Inventory considerations and safety stock\n",
+        "- Seasonal demand patterns\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n",
+        "\n",
+        "SPDX-License-Identifier: Apache-2.0\n",
+        "\n",
+        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "you may not use this file except in compliance with the License.\n",
+        "You may obtain a copy of the License at\n",
+        "\n",
+        "http://www.apache.org/licenses/LICENSE-2.0\n",
+        "\n",
+        "Unless required by applicable law or agreed to in writing, software\n",
+        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "See the License for the specific language governing permissions and\n",
+        "limitations under the License.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "cuopt_fresh",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

From c758c149d3c4ec37242614c72b090b9dfd783c45 Mon Sep 17 00:00:00 2001
From: anandhkb <aanandh@nvidia.com>
Date: Sun, 30 Nov 2025 17:22:49 -0800
Subject: [PATCH 2/2] Clear notebook outputs for cleaner diff

---
 cflp_cuopt_milp/cflp_cuopt_milp.ipynb | 1045 ++-----------------------
 1 file changed, 56 insertions(+), 989 deletions(-)

diff --git a/cflp_cuopt_milp/cflp_cuopt_milp.ipynb b/cflp_cuopt_milp/cflp_cuopt_milp.ipynb
index 84e6f69..73ad121 100644
--- a/cflp_cuopt_milp/cflp_cuopt_milp.ipynb
+++ b/cflp_cuopt_milp/cflp_cuopt_milp.ipynb
@@ -46,37 +46,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "        <div style=\"border:2px solid #4CAF50;padding:10px;border-radius:10px;background:#e8f5e9;\">\n",
-              "            <h3>✅ GPU is enabled</h3>\n",
-              "            <pre>| NVIDIA-SMI 535.161.08             Driver Version: 535.161.08   CUDA Version: 12.2     |</pre>\n",
-              "        </div>\n",
-              "        "
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "True"
-            ]
-          },
-          "execution_count": 1,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "import subprocess\n",
         "import html\n",
@@ -130,17 +102,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "✓ cuOpt is installed\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Check if cuOpt is installed, if not provide installation instructions\n",
         "try:\n",
@@ -161,32 +125,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: matplotlib in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (3.10.7)\n",
-            "Requirement already satisfied: seaborn in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (0.13.2)\n",
-            "Requirement already satisfied: contourpy>=1.0.1 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (1.3.3)\n",
-            "Requirement already satisfied: cycler>=0.10 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
-            "Requirement already satisfied: fonttools>=4.22.0 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (4.61.0)\n",
-            "Requirement already satisfied: kiwisolver>=1.3.1 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (1.4.9)\n",
-            "Requirement already satisfied: numpy>=1.23 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (2.2.6)\n",
-            "Requirement already satisfied: packaging>=20.0 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (25.0)\n",
-            "Requirement already satisfied: pillow>=8 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (12.0.0)\n",
-            "Requirement already satisfied: pyparsing>=3 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (3.2.5)\n",
-            "Requirement already satisfied: python-dateutil>=2.7 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\n",
-            "Requirement already satisfied: pandas>=1.2 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from seaborn) (2.3.3)\n",
-            "Requirement already satisfied: pytz>=2020.1 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
-            "Requirement already satisfied: tzdata>=2022.7 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
-            "Requirement already satisfied: six>=1.5 in /home/nfs/aanandh/miniconda3/envs/cuopt_fresh/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "%pip install matplotlib seaborn\n"
       ]
@@ -200,17 +141,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "✓ All imports successful\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "import numpy as np\n",
         "import pandas as pd\n",
@@ -258,21 +191,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Problem Size: 5 DCs, 20 customers\n",
-            "Total demand: 3244 pallets/week\n",
-            "\n",
-            "DC capacities: [ 970.  898.  932. 1005.  957.]\n",
-            "Fixed costs: $[102321. 101513. 102607.  98348.  96834.]\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Synthetic instance generation\n",
@@ -327,20 +248,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1500x500 with 3 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Visualize problem data\n",
         "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
@@ -404,19 +314,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Number of variables: 105\n",
-            "  - DC open/close (y): 5\n",
-            "  - Assignment (x): 100\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Problem and variable creation\n",
@@ -453,17 +353,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Objective set: Minimize (Fixed DC Costs + Transportation Costs)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Objective function\n",
@@ -487,20 +379,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Assignment constraints added: 20\n",
-            "Capacity constraints added: 5\n",
-            "\n",
-            "Total constraints: 25\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Constraints\n",
@@ -538,80 +419,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Starting optimization...\n",
-            "Problem size: 105 variables, 25 constraints\n",
-            "\n",
-            "Setting parameter time_limit to 3.000000e+02\n",
-            "Setting parameter mip_relative_gap to 1.000000e-04\n",
-            "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: x86_64, device archs: 70-real,75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
-            "CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, threads (physical/logical): 40/80, RAM: 279.15 GiB\n",
-            "CUDA 12.9, device: Tesla V100-SXM2-32GB (ID 0), VRAM: 31.74 GiB\n",
-            "CUDA device UUID: ffffffc2fffffffeffffffce48-7c2a-ffff\n",
-            "\n",
-            "Solving a problem with 25 constraints, 105 variables (105 integers), and 205 nonzeros\n",
-            "Problem scaling:\n",
-            "Objective coefficents range:          [2e+02, 1e+05]\n",
-            "Constraint matrix coefficients range: [1e+00, 1e+03]\n",
-            "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-            "Variable bounds range:                [0e+00, 1e+00]\n",
-            "\n",
-            "Original problem: 25 constraints, 105 variables, 205 nonzeros\n",
-            "Calling Papilo presolver\n",
-            "Presolve status: did not result in any changes\n",
-            "Presolve removed: 0 constraints, 0 variables, 0 nonzeros\n",
-            "Presolved problem: 25 constraints, 105 variables, 205 nonzeros\n",
-            "Papilo presolve time: 0.079425\n",
-            "Objective offset 0.000000 scaling_factor 1.000000\n",
-            "Running presolve!\n",
-            "After trivial presolve: 25 constraints, 105 variables, objective offset 0.000000.\n",
-            "Using 79 CPU threads for B&B\n",
-            "Solving LP root relaxation\n",
-            "Scaling matrix. Maximum column norm 1.000000e+00\n",
-            "Dual Simplex Phase 1\n",
-            "Dual feasible solution found.\n",
-            "Dual Simplex Phase 2\n",
-            " Iter     Objective           Num Inf.  Sum Inf.     Perturb  Time\n",
-            "    1 +1.5483404471657623e+03      20 3.80241542e+00 0.00e+00 0.01\n",
-            "\n",
-            "Root relaxation solution found in 40 iterations and 0.01s\n",
-            "Root relaxation objective +3.72800766e+05\n",
-            "\n",
-            "Strong branching using 79 threads and 7 fractional variables\n",
-            "Exploring the B&B tree using 19 best-first threads and 60 diving threads (79 threads)\n",
-            " | Explored | Unexplored |    Objective    |     Bound     | Depth | Iter/Node |   Gap    |  Time  |\n",
-            "B       175          107    +4.582990e+05    +3.728008e+05        8   2.5e+00     18.7%      0.02\n",
-            "B       359          233    +4.582492e+05    +3.728008e+05        9   2.4e+00     18.6%      0.02\n",
-            "B       359          257    +4.447577e+05    +3.728008e+05        9   2.5e+00     16.2%      0.02\n",
-            "B       522          372    +4.437249e+05    +3.759333e+05       16   2.6e+00     15.3%      0.02\n",
-            "B       531          369    +4.431174e+05    +3.759333e+05       14   2.6e+00     15.2%      0.02\n",
-            "B       561          368    +4.429949e+05    +3.764332e+05       16   2.5e+00     15.0%      0.02\n",
-            "D       686          362    +4.428134e+05    +3.770924e+05        1   3.0e+00     14.8%      0.02\n",
-            "B       996          326    +4.427737e+05    +3.861906e+05        9   3.5e+00     12.8%      0.02\n",
-            "D      1229          310    +4.424911e+05    +3.873834e+05        0   3.6e+00     12.5%      0.03\n",
-            "D      1437          283    +4.424627e+05    +3.877744e+05        0   3.7e+00     12.4%      0.03\n",
-            "D      1575          269    +4.420984e+05    +3.884250e+05        1   3.7e+00     12.1%      0.03\n",
-            "B      1591          271    +4.416565e+05    +3.884366e+05       19   3.7e+00     12.1%      0.03\n",
-            "B      2294           72    +4.412745e+05    +4.039022e+05       14   3.7e+00      8.5%      0.03\n",
-            "Explored 2322 nodes in 0.03s.\n",
-            "Absolute Gap 0.000000e+00 Objective 4.4127454429334094e+05 Lower Bound 4.4127454429334094e+05\n",
-            "Optimal solution found.\n",
-            "Post-solve status: succeeded\n",
-            "Solution objective: 441274.544293 , relative_mip_gap 0.000000 solution_bound 441274.544293 presolve_time 0.093789 total_solve_time 0.362014 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 2322 simplex_iterations 8559\n",
-            "\n",
-            "Optimization completed in 0.481 seconds\n",
-            "Solve status: 1\n",
-            "Best objective value: $441,274.54\n",
-            "Solve time (internal): 0.362s\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Solve\n",
@@ -648,19 +458,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Open DCs: [0, 2, 3, 4]\n",
-            "\n",
-            "DC loads: [925.   0. 420. 946. 953.]\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Solution extraction\n",
@@ -693,320 +493,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "======================================================================\n",
-            "                    DC SUMMARY\n",
-            "======================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>DC</th>\n",
-              "      <th>Open</th>\n",
-              "      <th>Capacity</th>\n",
-              "      <th>Load</th>\n",
-              "      <th>Utilization %</th>\n",
-              "      <th>Fixed Cost ($)</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>970.0</td>\n",
-              "      <td>925.0</td>\n",
-              "      <td>95.4</td>\n",
-              "      <td>102321.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1</td>\n",
-              "      <td>No</td>\n",
-              "      <td>898.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>101513.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>932.0</td>\n",
-              "      <td>420.0</td>\n",
-              "      <td>45.1</td>\n",
-              "      <td>102607.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>3</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>1005.0</td>\n",
-              "      <td>946.0</td>\n",
-              "      <td>94.2</td>\n",
-              "      <td>98348.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>4</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>957.0</td>\n",
-              "      <td>953.0</td>\n",
-              "      <td>99.6</td>\n",
-              "      <td>96834.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   DC Open  Capacity   Load  Utilization %  Fixed Cost ($)\n",
-              "0   0  Yes     970.0  925.0           95.4        102321.0\n",
-              "1   1   No     898.0    0.0            0.0        101513.0\n",
-              "2   2  Yes     932.0  420.0           45.1        102607.0\n",
-              "3   3  Yes    1005.0  946.0           94.2         98348.0\n",
-              "4   4  Yes     957.0  953.0           99.6         96834.0"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "======================================================================\n",
-            "                    CUSTOMER ASSIGNMENTS\n",
-            "======================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Customer</th>\n",
-              "      <th>Demand</th>\n",
-              "      <th>Assigned DC</th>\n",
-              "      <th>Transport Cost ($)</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0</td>\n",
-              "      <td>221.0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>3103.969202</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1</td>\n",
-              "      <td>113.0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>694.284083</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2</td>\n",
-              "      <td>216.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>4171.780505</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>3</td>\n",
-              "      <td>81.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>2331.171666</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>4</td>\n",
-              "      <td>215.0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1807.165698</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>5</td>\n",
-              "      <td>213.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>914.111449</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>6</td>\n",
-              "      <td>212.0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>2494.399882</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>7</td>\n",
-              "      <td>193.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2357.300628</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>8</td>\n",
-              "      <td>160.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>4003.817145</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>9</td>\n",
-              "      <td>199.0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2971.914652</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10</th>\n",
-              "      <td>10</td>\n",
-              "      <td>127.0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1156.862426</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>11</th>\n",
-              "      <td>11</td>\n",
-              "      <td>212.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>2260.513784</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>12</th>\n",
-              "      <td>12</td>\n",
-              "      <td>174.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3423.683757</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>13</th>\n",
-              "      <td>13</td>\n",
-              "      <td>158.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>200.529261</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>14</th>\n",
-              "      <td>14</td>\n",
-              "      <td>165.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1985.308388</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15</th>\n",
-              "      <td>15</td>\n",
-              "      <td>176.0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>2880.450359</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>16</td>\n",
-              "      <td>86.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>629.558633</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>17</th>\n",
-              "      <td>17</td>\n",
-              "      <td>103.0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>378.718005</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>18</th>\n",
-              "      <td>18</td>\n",
-              "      <td>121.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1850.279653</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>19</td>\n",
-              "      <td>99.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1548.340447</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "    Customer  Demand  Assigned DC  Transport Cost ($)\n",
-              "0          0   221.0            2         3103.969202\n",
-              "1          1   113.0            3          694.284083\n",
-              "2          2   216.0            4         4171.780505\n",
-              "3          3    81.0            4         2331.171666\n",
-              "4          4   215.0            3         1807.165698\n",
-              "5          5   213.0            0          914.111449\n",
-              "6          6   212.0            3         2494.399882\n",
-              "7          7   193.0            0         2357.300628\n",
-              "8          8   160.0            0         4003.817145\n",
-              "9          9   199.0            2         2971.914652\n",
-              "10        10   127.0            3         1156.862426\n",
-              "11        11   212.0            4         2260.513784\n",
-              "12        12   174.0            0         3423.683757\n",
-              "13        13   158.0            4          200.529261\n",
-              "14        14   165.0            4         1985.308388\n",
-              "15        15   176.0            3         2880.450359\n",
-              "16        16    86.0            0          629.558633\n",
-              "17        17   103.0            3          378.718005\n",
-              "18        18   121.0            4         1850.279653\n",
-              "19        19    99.0            0         1548.340447"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Create summary DataFrames\n",
         "dc_df = pd.DataFrame({\n",
@@ -1038,39 +527,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "======================================================================\n",
-            "                    COST BREAKDOWN\n",
-            "======================================================================\n",
-            "\n",
-            "📊 Cost Summary:\n",
-            "   • Total Fixed Costs: $400,110.38\n",
-            "   • Total Transport Costs: $41,164.16\n",
-            "   • Total Logistics Cost: $441,274.54\n",
-            "   • Fixed Cost %: 90.7%\n",
-            "   • Transport Cost %: 9.3%\n",
-            "\n",
-            "🏭 Network Design:\n",
-            "   • DCs Opened: 4 of 5\n",
-            "   • Customers Served: 20\n",
-            "   • Total Demand Met: 3,244 pallets/week\n",
-            "\n",
-            "📦 Utilization:\n",
-            "   • Average DC Utilization: 83.5%\n",
-            "   • DC 0: 95.4% (925/970)\n",
-            "   • DC 2: 45.1% (420/932)\n",
-            "   • DC 3: 94.2% (946/1005)\n",
-            "   • DC 4: 99.6% (953/957)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Calculate cost breakdown\n",
         "total_fixed_cost = sum(fixed_cost[i] for i in open_dcs)\n",
@@ -1113,20 +572,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 1600x1200 with 5 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Create comprehensive visualizations\n",
         "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
@@ -1226,18 +674,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Semi-Relaxed Problem - Number of variables: 105\n",
-            "Note: y_i remain INTEGER (facility decisions), x_ij are CONTINUOUS (fractional assignments)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Semi-Relaxation: Fractional assignments allowed\n",
@@ -1274,17 +713,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Semi-relaxed objective set (minimize total cost).\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Objective function for relaxed LP\n",
@@ -1308,18 +739,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Assignment constraints added (fractional allowed): 20\n",
-            "Capacity constraints added: 5\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Constraints for relaxed LP\n",
@@ -1347,67 +769,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Setting parameter time_limit to 3.000000e+02\n",
-            "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: x86_64, device archs: 70-real,75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
-            "CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, threads (physical/logical): 40/80, RAM: 278.96 GiB\n",
-            "CUDA 12.9, device: Tesla V100-SXM2-32GB (ID 0), VRAM: 31.74 GiB\n",
-            "CUDA device UUID: ffffffc2fffffffeffffffce48-7c2a-ffff\n",
-            "\n",
-            "Solving a problem with 25 constraints, 105 variables (5 integers), and 205 nonzeros\n",
-            "Problem scaling:\n",
-            "Objective coefficents range:          [2e+02, 1e+05]\n",
-            "Constraint matrix coefficients range: [1e+00, 1e+03]\n",
-            "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-            "Variable bounds range:                [0e+00, 1e+00]\n",
-            "\n",
-            "Original problem: 25 constraints, 105 variables, 205 nonzeros\n",
-            "Calling Papilo presolver\n",
-            "Presolve status: did not result in any changes\n",
-            "Presolve removed: 0 constraints, 0 variables, 0 nonzeros\n",
-            "Presolved problem: 25 constraints, 105 variables, 205 nonzeros\n",
-            "Papilo presolve time: 0.002642\n",
-            "Objective offset 0.000000 scaling_factor 1.000000\n",
-            "Running presolve!\n",
-            "After trivial presolve: 25 constraints, 105 variables, objective offset 0.000000.\n",
-            "Using 7 CPU threads for B&B\n",
-            "Solving LP root relaxation\n",
-            "Scaling matrix. Maximum column norm 1.000000e+00\n",
-            "Dual Simplex Phase 1\n",
-            "Dual feasible solution found.\n",
-            "Dual Simplex Phase 2\n",
-            " Iter     Objective           Num Inf.  Sum Inf.     Perturb  Time\n",
-            "    1 +7.2665688039950906e-13      20 1.07431630e+02 0.00e+00 0.00\n",
-            "\n",
-            "Root relaxation solution found in 40 iterations and 0.00s\n",
-            "Root relaxation objective +3.72800766e+05\n",
-            "\n",
-            "Strong branching using 7 threads and 1 fractional variables\n",
-            "Exploring the B&B tree using 1 best-first threads and 6 diving threads (7 threads)\n",
-            " | Explored | Unexplored |    Objective    |     Bound     | Depth | Iter/Node |   Gap    |  Time  |\n",
-            "B        20            2    +4.405980e+05    +3.728008e+05        4   6.5e+00     15.4%      0.00\n",
-            "Explored 28 nodes in 0.00s.\n",
-            "Absolute Gap 0.000000e+00 Objective 4.4059795365403272e+05 Lower Bound 4.4059795365403272e+05\n",
-            "Optimal solution found.\n",
-            "Post-solve status: succeeded\n",
-            "Solution objective: 440597.953654 , relative_mip_gap 0.000000 solution_bound 440597.953654 presolve_time 0.002730 total_solve_time 0.030602 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 28 simplex_iterations 197\n",
-            "Semi-Relaxed Solve status: 1\n",
-            "Semi-Relaxed Objective value: $440,597.95\n",
-            "Solve time: 0.031s\n",
-            "\n",
-            "Comparison:\n",
-            "  MILP (full integer) objective: $441,274.54\n",
-            "  Semi-relaxed objective:        $440,597.95\n",
-            "  Integrality gap: $676.59 (0.15%)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Solve the semi-relaxed problem\n",
@@ -1436,23 +800,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Open DCs (relaxed LP): [0, 2, 3, 4]\n",
-            "\n",
-            "Fractional assignments (customer -> DC: fraction):\n",
-            "  Customer 8: DC0: 0.772, DC4: 0.228\n",
-            "  Customer 9: DC2: 0.755, DC4: 0.245\n",
-            "\n",
-            "DC loads (relaxed LP): [970.   0. 371. 946. 957.]\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Analyze fractional assignment solution\n",
@@ -1503,26 +853,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Multi-period setup:\n",
-            "  Number of periods: 3\n",
-            "  Discount factor: 0.95\n",
-            "  Demand growth rate: 10.0% per period\n",
-            "  Cost inflation: 5.0% per period\n",
-            "\n",
-            "Total demand by period:\n",
-            "  Period 0: 3,244.0 pallets\n",
-            "  Period 1: 3,568.4 pallets\n",
-            "  Period 2: 3,925.2 pallets\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Multi-period problem setup\n",
@@ -1565,20 +898,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Multi-period - Number of variables: 330\n",
-            "  DC opening state variables (y): 15\n",
-            "  DC opening decision variables (z): 15\n",
-            "  Assignment variables (x): 300\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Multi-period problem and variable creation\n",
@@ -1634,21 +956,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "DC opening logic constraints added\n",
-            "Assignment constraints added: 60\n",
-            "Capacity constraints added: 15\n",
-            "\n",
-            "Total constraints: 115\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Multi-period constraints\n",
@@ -1719,17 +1029,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Multi-period objective set (minimize total discounted cost).\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Multi-period objective function\n",
@@ -1762,91 +1064,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Starting multi-period optimization...\n",
-            "Problem size: 330 variables, 115 constraints\n",
-            "\n",
-            "Setting parameter time_limit to 6.000000e+02\n",
-            "Setting parameter mip_relative_gap to 1.000000e-04\n",
-            "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: x86_64, device archs: 70-real,75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
-            "CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz, threads (physical/logical): 40/80, RAM: 278.96 GiB\n",
-            "CUDA 12.9, device: Tesla V100-SXM2-32GB (ID 0), VRAM: 31.74 GiB\n",
-            "CUDA device UUID: ffffffc2fffffffeffffffce48-7c2a-ffff\n",
-            "\n",
-            "Solving a problem with 115 constraints, 330 variables (330 integers), and 710 nonzeros\n",
-            "Problem scaling:\n",
-            "Objective coefficents range:          [2e+02, 1e+05]\n",
-            "Constraint matrix coefficients range: [1e+00, 1e+03]\n",
-            "Constraint rhs / bounds range:        [0e+00, 1e+00]\n",
-            "Variable bounds range:                [0e+00, 1e+00]\n",
-            "\n",
-            "Original problem: 115 constraints, 330 variables, 710 nonzeros\n",
-            "Calling Papilo presolver\n",
-            "Presolve status: reduced the problem\n",
-            "Presolve removed: 5 constraints, 5 variables, 10 nonzeros\n",
-            "Presolved problem: 110 constraints, 325 variables, 700 nonzeros\n",
-            "Papilo presolve time: 0.003391\n",
-            "Objective offset 0.000000 scaling_factor 1.000000\n",
-            "Running presolve!\n",
-            "After trivial presolve: 110 constraints, 325 variables, objective offset 0.000000.\n",
-            "Using 7 CPU threads for B&B\n",
-            "Solving LP root relaxation\n",
-            "Scaling matrix. Maximum column norm 1.545262e+00\n",
-            "Dual Simplex Phase 1\n",
-            "Dual feasible solution found.\n",
-            "Dual Simplex Phase 2\n",
-            " Iter     Objective           Num Inf.  Sum Inf.     Perturb  Time\n",
-            "    1 +1.7584595358888398e+03      60 1.18030442e+01 0.00e+00 0.00\n",
-            "\n",
-            "Root relaxation solution found in 113 iterations and 0.00s\n",
-            "Root relaxation objective +5.37659175e+05\n",
-            "\n",
-            "Strong branching using 7 threads and 29 fractional variables\n",
-            "Exploring the B&B tree using 1 best-first threads and 6 diving threads (7 threads)\n",
-            " | Explored | Unexplored |    Objective    |     Bound     | Depth | Iter/Node |   Gap    |  Time  |\n",
-            "D        77           47    +6.394054e+05    +6.311456e+05       15   1.1e+01      1.3%      0.01\n",
-            "D        83           49    +6.390234e+05    +6.312515e+05       14   1.1e+01      1.2%      0.01\n",
-            "D       123           73    +6.389470e+05    +6.314283e+05       15   1.2e+01      1.2%      0.01\n",
-            "B       148           92    +6.389256e+05    +6.315612e+05       27   1.2e+01      1.2%      0.02\n",
-            "B       188          118    +6.383388e+05    +6.317728e+05       28   1.3e+01      1.0%      0.02\n",
-            "B       212          132    +6.382313e+05    +6.318318e+05       34   1.3e+01      1.0%      0.03\n",
-            "B       244          154    +6.380229e+05    +6.318663e+05       29   1.3e+01      1.0%      0.03\n",
-            "B       290          186    +6.378457e+05    +6.319138e+05       32   1.3e+01      0.9%      0.03\n",
-            "B       424          246    +6.373905e+05    +6.319389e+05       32   1.3e+01      0.9%      0.05\n",
-            "D       439          253    +6.372714e+05    +6.319427e+05       13   1.3e+01      0.8%      0.05\n",
-            "D       465          263    +6.368023e+05    +6.319427e+05       12   1.3e+01      0.8%      0.05\n",
-            "D       553          311    +6.364657e+05    +6.319844e+05       14   1.3e+01      0.7%      0.06\n",
-            "D       564          316    +6.363915e+05    +6.319882e+05       15   1.3e+01      0.7%      0.06\n",
-            "D       580          324    +6.362587e+05    +6.319892e+05       13   1.3e+01      0.7%      0.07\n",
-            "D       595          333    +6.351174e+05    +6.319930e+05       11   1.3e+01      0.5%      0.07\n",
-            "D       639          349    +6.350675e+05    +6.319969e+05       13   1.3e+01      0.5%      0.07\n",
-            "D       653          355    +6.349933e+05    +6.319969e+05       14   1.3e+01      0.5%      0.07\n",
-            "D       679          365    +6.348605e+05    +6.320011e+05       12   1.3e+01      0.5%      0.08\n",
-            "D       796          416    +6.347367e+05    +6.320472e+05       11   1.3e+01      0.4%      0.09\n",
-            "D       833          427    +6.343005e+05    +6.320529e+05       11   1.3e+01      0.4%      0.09\n",
-            "D       888          446    +6.340285e+05    +6.320726e+05       13   1.3e+01      0.3%      0.10\n",
-            "D      1558          634    +6.339331e+05    +6.323445e+05       11   1.3e+01      0.3%      0.17\n",
-            "B      1587          641    +6.338670e+05    +6.323574e+05       25   1.3e+01      0.2%      0.17\n",
-            "D      1663          661    +6.334937e+05    +6.323695e+05       11   1.3e+01      0.2%      0.18\n",
-            "Explored 6591 nodes in 0.67s.\n",
-            "Absolute Gap 5.784958e+01 Objective 6.3349365915504191e+05 Lower Bound 6.3343580957984913e+05\n",
-            "Optimal solution found within relative MIP gap tolerance (1.0e-04)\n",
-            "Post-solve status: succeeded\n",
-            "Solution objective: 633493.659155 , relative_mip_gap 0.000091 solution_bound 633435.809580 presolve_time 0.003488 total_solve_time 2.611201 max constraint violation 0.000000 max int violation 0.000000 max var bounds violation 0.000000 nodes 6591 simplex_iterations 44835\n",
-            "\n",
-            "Optimization completed in 2.633 seconds\n",
-            "Multi-period Solve status: 1\n",
-            "Multi-period Objective value (discounted): $633,493.66\n",
-            "Solve time (internal): 2.611s\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Solve multi-period problem\n",
@@ -1872,20 +1092,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "DC Opening Schedule:\n",
-            "  Period 0: Open DCs [0, 1, 2, 3, 4]\n",
-            "  Period 1: No DCs opened\n",
-            "  Period 2: No DCs opened\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Analyze multi-period solution\n",
@@ -1920,107 +1129,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Multi-Period Cost Summary:\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Period</th>\n",
-              "      <th>DCs Opened</th>\n",
-              "      <th>Opening Cost ($)</th>\n",
-              "      <th>Transport Cost ($)</th>\n",
-              "      <th>Total Cost ($)</th>\n",
-              "      <th>Discounted Cost ($)</th>\n",
-              "      <th>Total Demand</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>501622.89</td>\n",
-              "      <td>40034.56</td>\n",
-              "      <td>541657.44</td>\n",
-              "      <td>541657.44</td>\n",
-              "      <td>3244.00</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.00</td>\n",
-              "      <td>45393.04</td>\n",
-              "      <td>45393.04</td>\n",
-              "      <td>43123.38</td>\n",
-              "      <td>3568.40</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.00</td>\n",
-              "      <td>53975.44</td>\n",
-              "      <td>53975.44</td>\n",
-              "      <td>48712.83</td>\n",
-              "      <td>3925.24</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Period  DCs Opened  Opening Cost ($)  Transport Cost ($)  Total Cost ($)  \\\n",
-              "0       0           5         501622.89            40034.56       541657.44   \n",
-              "1       1           0              0.00            45393.04        45393.04   \n",
-              "2       2           0              0.00            53975.44        53975.44   \n",
-              "\n",
-              "   Discounted Cost ($)  Total Demand  \n",
-              "0            541657.44       3244.00  \n",
-              "1             43123.38       3568.40  \n",
-              "2             48712.83       3925.24  "
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Total discounted cost: $633,493.66\n",
-            "Total undiscounted cost: $641,025.92\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Calculate costs by period\n",
         "costs_by_period = {}\n",
@@ -2073,20 +1184,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 1400x500 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# -----------------------------\n",
         "# Visualize multi-period solution\n",
@@ -2144,42 +1244,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "======================================================================\n",
-            "                    BUSINESS INSIGHTS\n",
-            "======================================================================\n",
-            "\n",
-            "📊 Single-Period MILP Results:\n",
-            "   • Optimal Cost: $441,274.54\n",
-            "   • DCs Opened: 4 of 5\n",
-            "   • Solve Time: 0.481 seconds\n",
-            "\n",
-            "📈 Multi-Period Results:\n",
-            "   • Total Discounted Cost: $633,493.66\n",
-            "   • Planning Horizon: 3 periods\n",
-            "   • DCs Eventually Opened: 5 of 5\n",
-            "   • Solve Time: 2.633 seconds\n",
-            "\n",
-            "🔄 Semi-Relaxation Insights:\n",
-            "   • Semi-Relaxed Objective: $440,597.95\n",
-            "   • Integrality Gap: $676.59 (0.15%)\n",
-            "\n",
-            "⚡ Performance:\n",
-            "   • GPU Acceleration: Enabled ✓\n",
-            "   • Single-period problem: 105 variables, 25 constraints\n",
-            "   • Multi-period problem: 330 variables, 115 constraints\n",
-            "\n",
-            "======================================================================\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "print(\"\\n\" + \"=\"*70)\n",
         "print(\" \" * 20 + \"BUSINESS INSIGHTS\")\n",