Scripts/Etapa1/Etapa1_Asignacion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Modelo de Asignación"
      ],
      "metadata": {
        "id": "QONfEwTE_4ME"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementación"
      ],
      "metadata": {
        "id": "tTKHi9nNAkw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install -i https://pypi.gurobi.com gurobipy\n",
        "!pip install haversine\n",
        "\n",
        "from haversine import haversine\n",
        "from gurobipy import*\n",
        "import pandas as pd\n",
        "\n",
        "# Cargar Datos\n",
        "coord_df = pd.read_excel('Coordenadas.xlsx', sheet_name = 'Hoja1')\n",
        "produccion = pd.read_excel('Produccion.xlsx', sheet_name = 'Hoja1')\n",
        "ejeX = list(coord_df['Longitud'])\n",
        "ejeY = list(coord_df['Latitud'])\n",
        "u = []\n",
        "for j in range(len(ejeX)):\n",
        "  u.append((ejeY[j], ejeX[j])) # Ubicación de cada finca\n",
        "\n",
        "# Definir los conjuntos\n",
        "n = 180 # Número de fincas\n",
        "m = 2 # Número de plantas postcosecha\n",
        "F = [i for i in range(n)] # Fincas\n",
        "P = [j for j in range(m)] # Plantas\n",
        "\n",
        "# Definir los parámetros\n",
        "# Distancia entre cada finca y cada planta post cosecha (km)\n",
        "centrosIniciales = [(7.019472, -71.384889), (6.6, -71.65)]\n",
        "contFinca = 0\n",
        "d = {}\n",
        "for fincaAct in u:\n",
        "  contCentro = 0\n",
        "  for centro in centrosIniciales:\n",
        "    # Vector con las distancias entre la finca actual y cada planta post cosecha\n",
        "    d[(contFinca, contCentro)] = haversine(fincaAct, centro)\n",
        "    contCentro += 1\n",
        "  contFinca += 1\n",
        "\n",
        "\n",
        "# Capacidad de procesamiento de cada planta (ton)\n",
        "c = [1300, 600]\n",
        "\n",
        "# Producción de cada finca (ton)\n",
        "p = list(produccion['Producción'])\n",
        "\n",
        "# Crear el modelo\n",
        "modeloAsignacion = Model('Asignación')\n",
        "\n",
        "# Crear las variables de decisión\n",
        "x = modeloAsignacion.addVars(F, P, vtype = GRB.BINARY)\n",
        "\n",
        "# Restricciones del Problema\n",
        "# La producción de las fincas no puede superar la capacidad de las plantas\n",
        "for j in P:\n",
        "  modeloAsignacion.addConstr(quicksum(x[i, j] * p[i] for i in F) <= c[j])\n",
        "# Cada finca sólo puede ser asignada a una única planta\n",
        "for i in F:\n",
        "  modeloAsignacion.addConstr(quicksum(x[i, j] for j in P) == 1)\n",
        "\n",
        "# Función Objetivo\n",
        "modeloAsignacion.setObjective(quicksum(x[i, j] * d[i, j] for i in F for j in P), GRB.MINIMIZE)\n",
        "\n",
        "# Resolver\n",
        "modeloAsignacion.optimize()\n",
        "print('\\n')"
      ],
      "metadata": {
        "id": "jg-MH5fUEO_m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "281879d4-ea2b-4b63-992d-eef84c66ed14"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.gurobi.com, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gurobipy\n",
            "  Downloading gurobipy-10.0.0-cp38-cp38-manylinux2014_x86_64.whl (12.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.8 MB 7.5 MB/s \n",
            "\u001b[?25hInstalling collected packages: gurobipy\n",
            "Successfully installed gurobipy-10.0.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting haversine\n",
            "  Downloading haversine-2.7.0-py2.py3-none-any.whl (6.9 kB)\n",
            "Installing collected packages: haversine\n",
            "Successfully installed haversine-2.7.0\n",
            "Restricted license - for non-production use only - expires 2024-10-28\n",
            "Gurobi Optimizer version 10.0.0 build v10.0.0rc2 (linux64)\n",
            "\n",
            "CPU model: Intel(R) Xeon(R) CPU @ 2.20GHz, instruction set [SSE2|AVX|AVX2]\n",
            "Thread count: 1 physical cores, 2 logical processors, using up to 2 threads\n",
            "\n",
            "Optimize a model with 182 rows, 360 columns and 720 nonzeros\n",
            "Model fingerprint: 0x5ace6674\n",
            "Variable types: 0 continuous, 360 integer (360 binary)\n",
            "Coefficient statistics:\n",
            "  Matrix range     [1e+00, 1e+01]\n",
            "  Objective range  [8e-01, 7e+01]\n",
            "  Bounds range     [1e+00, 1e+00]\n",
            "  RHS range        [1e+00, 1e+03]\n",
            "Found heuristic solution: objective 6914.5418037\n",
            "Presolve removed 182 rows and 360 columns\n",
            "Presolve time: 0.00s\n",
            "Presolve: All rows and columns removed\n",
            "\n",
            "Explored 0 nodes (0 simplex iterations) in 0.02 seconds (0.00 work units)\n",
            "Thread count was 1 (of 2 available processors)\n",
            "\n",
            "Solution count 2: 4714.9 6914.54 \n",
            "\n",
            "Optimal solution found (tolerance 1.00e-04)\n",
            "Best objective 4.714898200083e+03, best bound 4.714898200083e+03, gap 0.0000%\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resultados"
      ],
      "metadata": {
        "id": "OJcK59nGbgSv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Imprimir y graficar los resultados\n",
        "print(f'Función Objetivo: {modeloAsignacion.getObjective().getValue()}')\n",
        "print('\\n')\n",
        "for j in P:  \n",
        "  cP = 0\n",
        "  for i in F:\n",
        "    if x[i, j].x == 1:\n",
        "      cP += 1\n",
        "  print(f'Total Fincas Planta {j+1} = {cP}')\n",
        "  print('\\n')  \n",
        "\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(10)\n",
        "\n",
        "colores = ('green', 'orange')\n",
        "for j in P:\n",
        "  for i in F:\n",
        "    if x[i, j].x == 1:\n",
        "      plt.scatter(u[i][1], u[i][0], color = colores[j], marker = \".\", s = 50)\n",
        "\n",
        "plt.xlabel('Longitud', fontsize = 15)\n",
        "plt.ylabel('Latitud', fontsize = 15)\n",
        "plt.title('Ubicación de fincas asignadas a cada planta', fontsize = 20)\n",
        "plt.scatter(centrosIniciales[0][1], centrosIniciales[0][0], color = (240/255, 0/255, 255/255), marker = \"^\", s = 250)\n",
        "plt.annotate('Central 1', (-71.374889, 7.029472), fontsize = 13)\n",
        "plt.scatter(centrosIniciales[1][1], centrosIniciales[1][0], color = (240/255, 0/255, 255/255), marker = \"^\", s = 250)\n",
        "plt.annotate('Central 2', (-71.645, 6.605), fontsize = 13)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "Ekg5u5UqbfxE",
        "outputId": "062122fc-21c8-4f6f-ca6a-7081a1f7b20c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Función Objetivo: 4714.898200083451\n",
            "\n",
            "\n",
            "Total Fincas Planta 1 = 85\n",
            "\n",
            "\n",
            "Total Fincas Planta 2 = 95\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}