{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8f73441b",
   "metadata": {},
   "source": [
    "# **Project assignment problem**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e386e3",
   "metadata": {},
   "source": [
    "***assignment.ipynb***\n",
    "\n",
    "One-to-one assignment of $n$ projects to $n$ persons to maximize the overall preference level. \n",
    "\n",
    "&copy; Copyright 2025 Fair Isaac Corporation\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.\n",
    " \n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
    "\n",
    "This example uses FICO&reg; Xpress software. By running it, you agree to the Community License terms of the [Xpress Shrinkwrap License Agreement](https://www.fico.com/en/shrinkwrap-license-agreement-fico-xpress-optimization-suite-on-premises) with respect to the FICO&reg; Xpress software. See the [licensing options](https://www.fico.com/en/fico-xpress-trial-and-licensing-options) overview for additional details and information about obtaining a paid license."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f871c0",
   "metadata": {},
   "source": [
    "## Problem description and formulation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89d8b40",
   "metadata": {},
   "source": [
    "There are $n$ persons and $n$ projects.  Each person $i \\in \\mathcal{I}$ ranks every project $j \\in \\mathcal{J}$ with a non-negative integer preference score $p_{i,j}$.  The goal is to find a one‑to‑one matching between persons and projects that maximizes the total satisfaction across all assignments. \n",
    "\n",
    "Let $x_{i,j}$ be the set of **binary decision variables** indicating if person $i$ is assigned to project $j$ (=1), or not (=0). The objective function aims to maximize the total satisfaction:\n",
    "\n",
    "$$\n",
    "\\max \\sum_{i\\in \\mathcal{I}}\\sum_{j\\in \\mathcal{J}} p_{i,j}\\,x_{i,j}.\n",
    "$$\n",
    "\n",
    "Subject to the following constraints:\n",
    "\n",
    "* One project per person:\n",
    "   $$\n",
    "   \\sum_{j\\in \\mathcal{J}} x_{i,j} = 1,\n",
    "   \\quad\\forall\\,i\\in \\mathcal{I}.\n",
    "   $$\n",
    "\n",
    "* One person per project:\n",
    "   $$\n",
    "   \\sum_{i\\in \\mathcal{I}} x_{i,j} = 1,\n",
    "   \\quad\\forall\\,j\\in \\mathcal{J}.\n",
    "   $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b878cac",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f75243",
   "metadata": {},
   "source": [
    "First, we import the necessary libraries for the notebook. We then define the preference matrix `PREF`, where each entry `PREF[i, j]` captures how much person $i$ values project $j$. From this matrix, we infer the number of persons and projects, and establish simple Python ranges (`people` and `projects`) to list them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6f7e486",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xpress as xp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assignment\n",
    "# Preference matrix: rows = persons, columns = projects\n",
    "PREF = np.array([\n",
    "    [1, 2, 3, 5, 4],\n",
    "    [3, 2, 5, 4, 1],\n",
    "    [3, 4, 1, 5, 2],\n",
    "    [4, 3, 2, 5, 1],\n",
    "    [2, 3, 5, 4, 1]\n",
    "])\n",
    "\n",
    "num_people, num_projects = PREF.shape\n",
    "people = range(num_people)\n",
    "projects = range(num_projects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fa529d",
   "metadata": {},
   "source": [
    "## Model implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db71dc31",
   "metadata": {},
   "source": [
    "We instantiate an Xpress model named **Assignment**, and create the set of binary decision variables. By passing integer arguments to [p.addVariables()](https://www.fico.com/fico-xpress-optimization/docs/latest/solver/optimizer/python/HTML/problem.addVariables.html), a *NumPy* array of variables (`assign`) is created.\n",
    "\n",
    "The objective and constraints are then created and added to the problem by passing the corresponding expressions, using list comprehension, to [p.setObjective()](https://www.fico.com/fico-xpress-optimization/docs/latest/solver/optimizer/python/HTML/problem.setObjective.html) and [p.addConstraint()](https://www.fico.com/fico-xpress-optimization/docs/latest/solver/optimizer/python/HTML/problem.addConstraint.html), respectively.\n",
    "\n",
    "After building the model, we turn off solver logging using the `OUTPUTLOG` control and call [p.optimize()](https://www.fico.com/fico-xpress-optimization/docs/latest/solver/optimizer/python/HTML/problem.optimize.html) to solve the problem, then extract and display the total satisfaction as well as each individual assignment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01fd7e29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total satisfaction score: 21.0\n",
      "Person 1: project 5\n",
      "Person 2: project 3\n",
      "Person 3: project 4\n",
      "Person 4: project 1\n",
      "Person 5: project 2\n"
     ]
    }
   ],
   "source": [
    "p = xp.problem(name=\"Assignment\")\n",
    "\n",
    "# Decision variables: matrix of binary variables x[i,j]\n",
    "assign = p.addVariables(num_people, num_projects, vartype=xp.binary, name=\"x\")\n",
    "\n",
    "# Objective Function: Maximum overall preference\n",
    "obj = xp.Sum(PREF[i, j] * assign[i, j] for i in people for j in projects)\n",
    "p.setObjective(obj, sense=xp.maximize)\n",
    "\n",
    "# Constraint 1: one project per person\n",
    "p.addConstraint(xp.Sum(assign[i, j] for j in projects) == 1 for i in people)\n",
    "\n",
    "# Constraint 2: one person per project\n",
    "p.addConstraint(xp.Sum(assign[i, j] for i in people) == 1 for j in projects)\n",
    "\n",
    "# Solve the problem\n",
    "p.controls.outputlog = 0        # Turn off solver logging for cleaner output\n",
    "p.optimize()\n",
    "\n",
    "# Get solution\n",
    "sol = p.getSolution(assign)\n",
    "total_satisfaction = p.attributes.objval\n",
    "\n",
    "# Print results\n",
    "print(\"Total satisfaction score:\", total_satisfaction)\n",
    "for i in people:\n",
    "    j = int(np.argmax(sol[i]))\n",
    "    print(f\"Person {i+1}: project {j+1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d4ae45",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da2dca4",
   "metadata": {},
   "source": [
    "After running the code cell below, we can visualize the assignment as a bipartite graph with persons on the left, projects on the right, and lines whose **thickness reflects preference connecting each matched pair** connecting both sides.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "838ae7cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "left_x, right_x = 0, 2\n",
    "\n",
    "y_people = np.arange(1, num_people + 1)\n",
    "y_projects = np.arange(1, num_projects + 1)\n",
    "ax.scatter([left_x]*num_people, y_people, s=100, label=\"Persons\")\n",
    "ax.scatter([right_x]*num_projects, y_projects, s=100, label=\"Projects\")\n",
    "\n",
    "# Add node labels\n",
    "txt_offset = 0.1\n",
    "for i in people:\n",
    "    ax.text(left_x - txt_offset, i+1, str(i+1), ha='right', va='center')\n",
    "for j in projects:\n",
    "    ax.text(right_x + txt_offset, j+1, str(j+1), ha='left', va='center')\n",
    "\n",
    "for i in people:\n",
    "    j = int(np.argmax(sol[i]))\n",
    "    pref = PREF[i, j]\n",
    "    ax.plot([left_x, right_x], [i+1, j+1], linewidth=pref)\n",
    "ax.axis('off')\n",
    "ax.set_title('Assignment of Persons to Projects')\n",
    "ax.legend(loc='upper center')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
