Scripts/ModeloCompleto/ModeloCompletoC1.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": [
        "# Ubicación de las Fincas en el Espacio"
      ],
      "metadata": {
        "id": "NHtgaarZkukG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# Cargar Coordenadas y Producción\n",
        "coord_df = pd.read_excel('Coordenadas.xlsx', sheet_name = 'Hoja1')\n",
        "produccion = pd.read_excel('Produccion.xlsx', sheet_name = 'Hoja1')"
      ],
      "metadata": {
        "id": "yYZLSC2mk3Z9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(10)\n",
        "centrosIniciales = [(7.019472, -71.384889), (6.6, -71.65)]\n",
        "plt.scatter(coord_df['Longitud'], coord_df['Latitud'], c =\"gray\")\n",
        "plt.scatter(centrosIniciales[0][1], centrosIniciales[0][0], color = (240/255, 0/255, 255/255), marker = \"^\", s = 250)\n",
        "plt.scatter(centrosIniciales[1][1], centrosIniciales[1][0], color = (240/255, 0/255, 255/255), marker = \"^\", s = 250)\n",
        "plt.xlabel('Longitud', fontsize = 15)\n",
        "plt.ylabel('Latitud', fontsize = 15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "lvVja9tDk1Np",
        "outputId": "8e783f1f-d121-4ff1-f42a-935bf2780638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Asignación Fincas a Plantas Post Cosecha"
      ],
      "metadata": {
        "id": "A3y-gep6kZLi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementación"
      ],
      "metadata": {
        "id": "8sE03QqtlkDq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install -i https://pypi.gurobi.com gurobipy\n",
        "!pip install haversine"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ARnUMfRlqaB",
        "outputId": "825a2bce-154d-4a7f-fe13-f8aa11e614ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.gurobi.com, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gurobipy in /usr/local/lib/python3.8/dist-packages (10.0.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: haversine in /usr/local/lib/python3.8/dist-packages (2.7.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wRxHPxz9kWaF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27e144bb-9758-4f69-c4ce-2ac141a38f30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gurobi Optimizer version 10.0.0 build v10.0.0rc2 (linux64)\n",
            "\n",
            "CPU model: AMD EPYC 7B12, 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"
          ]
        }
      ],
      "source": [
        "from haversine import haversine\n",
        "from gurobipy import*\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 = [(6.9, -71.6), (6.75, -71.45)]\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')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resultados"
      ],
      "metadata": {
        "id": "ZM27iUUOlwFd"
      }
    },
    {
      "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",
        "listaFincasPlanta1 = []\n",
        "for j in P:  \n",
        "  cP = 0\n",
        "  for i in F:\n",
        "    if x[i, j].x == 1:\n",
        "      cP += 1\n",
        "      if j == 0:\n",
        "        listaFincasPlanta1.append(i)\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",
        "  plt.scatter(centrosIniciales[j][1], centrosIniciales[j][0], color = (240/255, 0/255, 255/255), \n",
        "              marker = \"^\", s = 250)\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.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "cyAXxIcdl1MV",
        "outputId": "100eabcb-6698-4d1e-ace6-169fe230ecd5"
      },
      "execution_count": null,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementación Partinioning Around Medioids (PAM)"
      ],
      "metadata": {
        "id": "MEFnkL4BmXD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import copy\n",
        "\n",
        "# Función para calcular distancia (disimilaridad) entre cada finca y los medioids iniciales\n",
        "def disimilaridadFincaMedioids(fincas, medioids):\n",
        "  dictDist = {}\n",
        "  dictAsign = {}\n",
        "  for fincaAct in fincas:\n",
        "    if fincaAct not in medioids:\n",
        "      dictDist[fincaAct] = []\n",
        "      for medioidAct in medioids:\n",
        "        if haversine(fincaAct, medioidAct) != 0: # Agrega fincas sin contar el medioid actual\n",
        "          dictDist[fincaAct].append(haversine(fincaAct, medioidAct))\n",
        "        dictAsign[medioidAct] = [[], []]\n",
        "  return dictDist, dictAsign\n",
        "\n",
        "# Función para asignar cada finca al medioid más cercano con disponibilidad de fincas y la distancia de cada medioid a las fincas\n",
        "def asignarFincasMedioid(fincas, medioids, dictDist, dictAsign):\n",
        "  for fincaAct in fincas:\n",
        "    if fincaAct not in medioids:\n",
        "      numFincas = len(fincas)\n",
        "      nMedioids = len(medioids)\n",
        "      tope = (numFincas // nMedioids) - 2 # El máximo de fincas que no debería superar cada cluster\n",
        "      posicionesKSirven = []\n",
        "      for llave in list(dictAsign.keys()):\n",
        "        if len(dictAsign[llave][0]) <= tope:\n",
        "          posicionesKSirven.append(medioids.index(llave)) # Posiciones de medioids que respetan el tope\n",
        "      listaDistAct = dictDist[fincaAct] # Lista de distancias de la finca a cada medioid\n",
        "      if len(posicionesKSirven) > 0:\n",
        "        for i in range(len(listaDistAct)):\n",
        "          if i not in posicionesKSirven:\n",
        "            listaDistAct[i] = 70000\n",
        "      minimoAct = min(listaDistAct) # El medioid más cercano a la finca actual\n",
        "      posiMedioid = listaDistAct.index(minimoAct) # Argmin de la línea anterior\n",
        "      posiFinca = fincas.index(fincaAct) # Número de la finca actual\n",
        "      dictAsign[medioids[posiMedioid]][0].append(minimoAct) # Agregar la distancia de la finca agregada\n",
        "      dictAsign[medioids[posiMedioid]][1].append(posiFinca) # Agregar finca al medioid\n",
        "  return dictAsign\n",
        "\n",
        "# Función para eliminar un medioid en caso de que quede sin fincas\n",
        "def eliminarMedioid(dictAsign):\n",
        "  for medioidAct in list(dictAsign.keys()):\n",
        "    listaFincasAct = dictAsign[medioidAct][1]\n",
        "    if len(listaFincasAct) == 0:\n",
        "      del dictAsign[medioidAct]\n",
        "  return dictAsign\n",
        "\n",
        "# Función para calcular costo total\n",
        "def costoTotal(dictAsign):\n",
        "  costoActual = 0\n",
        "  for llave in list(dictAsign.keys()):\n",
        "    costoActual += sum(dictAsign[llave][0])\n",
        "  return costoActual\n",
        "\n",
        "# Función del Swap\n",
        "def swapFunction(fincas, dictAsign):\n",
        "  dictAsignAct = copy.deepcopy(dictAsign)\n",
        "  # Recorrer cada cluster e intercambiar una finca propia de este con cada medioid actual \n",
        "  for medioidAct in list(dictAsign.keys()):\n",
        "    listaFincasAct = dictAsign[medioidAct][1] # Lista de fincas en cada cluster\n",
        "    # Asegurarse que el medioid y la finca elegida no sean iguales\n",
        "    termino = False\n",
        "    fincaRand = listaFincasAct[random.randrange(len(listaFincasAct))] # Selección de una nueva finca como medioid\n",
        "    while termino == False:\n",
        "      if fincas[fincaRand] != medioidAct:\n",
        "        termino = True\n",
        "      else:\n",
        "        fincaRand = listaFincasAct[random.randrange(len(listaFincasAct))] # Selección de una nueva finca como medioid\n",
        "    dictAsignAct[fincas[fincaRand]] = dictAsignAct[medioidAct] \n",
        "    del(dictAsignAct[medioidAct]) # Reemplazar el medioid actual por una finca del cluster\n",
        "    dictAsignAct[fincas[fincaRand]][1].append(fincas.index(medioidAct)) # Guardar el medioid antiguo como finca\n",
        "  return dictAsignAct"
      ],
      "metadata": {
        "id": "Hnl-CK6_35NN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resultados Finales"
      ],
      "metadata": {
        "id": "8mtNpzDwmU_y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Central 1"
      ],
      "metadata": {
        "id": "HK14a4pQpmvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from gurobipy import Model, GRB, quicksum\n",
        "import math\n",
        "# Número de Clusters por Planta Post-Cosecha\n",
        "KTot = [5, 10, 15, 20]\n",
        "# Capacidad de los vehículos \n",
        "QTot = [16, 28, 40, 52]"
      ],
      "metadata": {
        "id": "MAfy8ZVw-uNa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f = open ('ResultadosCentral1.txt','w')\n",
        "# Diccionario para guardar los valores de la función objetivo (distancia) por cada pareja (K, Q)\n",
        "dictFOTotalC1 = {}\n",
        "iterPAM = 0\n",
        "for K1 in KTot:\n",
        "  for Q in QTot:\n",
        "    # PAM para Central 1\n",
        "    # Lista con la ubicación de cada Finca\n",
        "    n = len(listaFincasPlanta1)\n",
        "    lats = list(coord_df['Latitud'])\n",
        "    longs = list(coord_df['Longitud'])\n",
        "    ubFincas1 = []\n",
        "    for j in listaFincasPlanta1:\n",
        "      ubFincas1.append((lats[j], longs[j]))\n",
        "\n",
        "    # =================================================================== #\n",
        "    #============================ FASE BUILD ============================ #\n",
        "    # =================================================================== #\n",
        "    # Definir cantidad de clusters y los medioids iniciales\n",
        "    medioidsIniciales = [] # Asignar de forma aleatoria los K medioids a fincas existentes\n",
        "    dictAsignFincasMedioids = {} # Guarda las fincas asignadas a cada medioid\n",
        "    listaRand = random.sample(range(n), K1)\n",
        "    for aleAct in listaRand:\n",
        "      medioidsIniciales.append(ubFincas1[aleAct])\n",
        "    # Calcular distancia (disimilaridad) entre cada finca y los medioids iniciales\n",
        "    dictDistFincasMedioids, dictAsignFincasMedioids = disimilaridadFincaMedioids(ubFincas1, medioidsIniciales)\n",
        "    # Asignar cada finca al medioid más cercano y calcular el costo (distancia) total\n",
        "    dictAsignFincasMedioids = asignarFincasMedioid(ubFincas1, medioidsIniciales, dictDistFincasMedioids, dictAsignFincasMedioids)\n",
        "    # Si un medioid se queda sin fincas se elimina\n",
        "    dictAsignFincasMedioids = eliminarMedioid(dictAsignFincasMedioids)\n",
        "    # Calcular el costo total inicial de asignación\n",
        "    mejorCosto = costoTotal(dictAsignFincasMedioids)\n",
        "    mejorDictDist = copy.deepcopy(dictDistFincasMedioids)\n",
        "    mejorDictAsign = copy.deepcopy(dictAsignFincasMedioids)\n",
        "\n",
        "\n",
        "    # =================================================================== #\n",
        "    # ============================ FASE SWAP ============================ #\n",
        "    # =================================================================== #\n",
        "\n",
        "    iterMax = 1000\n",
        "    i = 0\n",
        "\n",
        "    while i < iterMax:\n",
        "\n",
        "      # Seleccionar k puntos no-medioids, convertirlos en medioids y devolver los anteriores a fincas corrientes\n",
        "      dictAsignFincasMedioidsAct = swapFunction(ubFincas1, mejorDictAsign)\n",
        "      # Calcular distancia (disimilaridad) entre cada finca y los medioids actuales\n",
        "      medioidsActuales = list(dictAsignFincasMedioidsAct.keys())\n",
        "      dictDistFincasMedioids,  dictAsignFincasMedioids = disimilaridadFincaMedioids(ubFincas1, medioidsActuales)\n",
        "      # Asignar cada finca al medioid más cercano y calcular el costo (distancia) total\n",
        "      dictAsignFincasMedioids = asignarFincasMedioid(ubFincas1, medioidsActuales, dictDistFincasMedioids, dictAsignFincasMedioids)\n",
        "      # Si un medioid se queda sin fincas se elimina\n",
        "      dictAsignFincasMedioids = eliminarMedioid(dictAsignFincasMedioids)\n",
        "      # Calcular el costo total actual de asignación\n",
        "      costoActual = costoTotal(dictAsignFincasMedioids)\n",
        "      if costoActual < mejorCosto:\n",
        "        mejorCosto = costoActual\n",
        "        mejorDictDist = copy.deepcopy(dictDistFincasMedioids)\n",
        "        mejorDictAsign = copy.deepcopy(dictAsignFincasMedioids)\n",
        "      i += 1 \n",
        "\n",
        "    mejorDictAsign1 = copy.deepcopy(mejorDictAsign)\n",
        "    iterPAM += 1\n",
        "    print(f'Acabó PAM iteración {iterPAM}')\n",
        "    f.write(f'Acabó PAM iteración {iterPAM}')\n",
        "    f.write(f'\\n')\n",
        "\n",
        "    # Guardar fincas por cada cluster asociado a la planta 1\n",
        "    listaFincasClusterP1 = []\n",
        "    for llave in list(mejorDictAsign1.keys()):\n",
        "      listaFincasClusterP1.append(mejorDictAsign1[llave][1] + [ubFincas1.index(llave)])\n",
        "\n",
        "    # CVRP Para Central 1\n",
        "    vFOClustersPlanta1 = []\n",
        "    nCamionesClustersPlanta1 = []\n",
        "    arcosSoluTot = []\n",
        "    ubFincasTot = []\n",
        "    a = 0\n",
        "    print(f'Valores F.O por cada Cluster de la iteración {iterPAM}:')\n",
        "    f.write(f'Valores F.O por cada Cluster de la iteración {iterPAM}:')\n",
        "    f.write(f'\\n')\n",
        "    for lali in listaFincasClusterP1:\n",
        "      \n",
        "      # Conjuntos\n",
        "      n = len(lali)\n",
        "      N = [i for i in range(1, n + 1)] # Número de clientes\n",
        "      V = [0] + N # Número de nodos en la red (incluyendo el centro post cosecha)\n",
        "      A = [(i, j) for i in V for j in V if i != j] # Arcos en la red\n",
        "\n",
        "      # Parámetros\n",
        "      # Matriz de distancia:\n",
        "      lats = list(coord_df['Latitud'])\n",
        "      longs = list(coord_df['Longitud'])\n",
        "      ubFincas = []\n",
        "      ubFincas.append(centrosIniciales[0])\n",
        "      for i in lali:\n",
        "        ubFincas.append((lats[i], longs[i]))\n",
        "      d = {(i, j):float(haversine(ubFincas[i], ubFincas[j])) for (i, j) in A}\n",
        "      # Producción de las fincas\n",
        "      produc = list(produccion['Producción'])\n",
        "      p = []\n",
        "      p.append(produc[0])\n",
        "      for i in lali:\n",
        "        p.append(produc[i])\n",
        "\n",
        "      # Número de vehículos\n",
        "      K = math.ceil((sum(p)/Q))\n",
        "\n",
        "      # Crear el modelo\n",
        "      modeloRuteo = Model('CVRP')\n",
        "      modeloRuteo.setParam(\"OutputFlag\",0)\n",
        "\n",
        "      # Variables de decisión\n",
        "      x = modeloRuteo.addVars(A, vtype=GRB.BINARY, name = 'x') # Si el arco (i, j) debe tomar valor\n",
        "      u = modeloRuteo.addVars(N, vtype=GRB.CONTINUOUS, name = 'u') # Variable continua para eliminar los subtours\n",
        "\n",
        "      # Restricciones\n",
        "      # Sólo puede existir un arco entrante hacia cada una de las fincas\n",
        "      modeloRuteo.addConstrs(quicksum(x[i, j] for i in V if i != j) == 1 for j in N)\n",
        "      # Sólo puede existir un arco saliente desde cada una de las fincas\n",
        "      modeloRuteo.addConstrs(quicksum(x[i, j] for j in V if j != i) == 1 for i in N)\n",
        "      # Sólo puede salir un número de vehículos K de la planta post cosecha\n",
        "      modeloRuteo.addConstr(quicksum(x[0, j] for j in N) == K)\n",
        "      # Sólo puede llegar un número de vehículos K hacia la planta post cosecha\n",
        "      modeloRuteo.addConstr(quicksum(x[i, 0] for i in N) == K)\n",
        "      # Eliminar soubtours\n",
        "      modeloRuteo.addConstrs((x[i, j] == 1) >> (u[i] + p[j] == u[j]) for (i, j) in A if i != 0 and j != 0)\n",
        "      # Límites de las variables u\n",
        "      modeloRuteo.addConstrs(u[i] >= p[i] for i in N)\n",
        "      modeloRuteo.addConstrs(u[i] <= Q for i in N)\n",
        "\n",
        "      # Función Objetivo\n",
        "      modeloRuteo.modelSense = GRB.MINIMIZE\n",
        "      modeloRuteo.setObjective(quicksum(x[i, j]*d[i, j] for (i, j) in A))\n",
        "\n",
        "      modeloRuteo.Params.MIPGap = 0.1\n",
        "      modeloRuteo.Params.TimeLimit = 20  # seconds\n",
        "      modeloRuteo.optimize()\n",
        "      #modeloRuteo.computeIIS()\n",
        "      #modeloRuteo.write(\"model.ilp\")\n",
        "\n",
        "      # Guardar valor de la función objetivo\n",
        "      try:\n",
        "        vFOClustersPlanta1.append(round(modeloRuteo.getObjective().getValue(), 2))\n",
        "        print(f'\\t FO cluster {a}: {round(modeloRuteo.getObjective().getValue(), 2)},  Número de Vehículos: {K}')\n",
        "        f.write(f'\\t FO cluster {a}: {round(modeloRuteo.getObjective().getValue(), 2)},  Número de Vehículos: {K}')\n",
        "        f.write(f'\\n')\n",
        "        a += 1\n",
        "        # Guardar las rutas creadas, las ubicaciones y los camiones\n",
        "        arcosSolu = []\n",
        "        for (i, j) in A:\n",
        "          if x[i, j].x > 0.9:\n",
        "            arcosSolu.append((i, j))\n",
        "        arcosSoluTot.append(arcosSolu)\n",
        "        ubFincasTot.append(ubFincas)\n",
        "        nCamionesClustersPlanta1.append(K)\n",
        "      except:\n",
        "        print(f'\\t FO cluster {a}: Modelo Infactible')\n",
        "        f.write(f'\\t FO cluster {a}: Modelo Infactible')\n",
        "        f.write(f'\\n')\n",
        "        vFOClustersPlanta1 = 'Infactible'\n",
        "        arcosSoluTot = []\n",
        "        ubFincasTot = []\n",
        "        nCamionesClustersPlanta1 = [0]\n",
        "        a += 1\n",
        "\n",
        "    # Resultados CVRP Central 1\n",
        "    dictFOTotalC1[K1, Q] = [vFOClustersPlanta1, max(nCamionesClustersPlanta1), arcosSoluTot, ubFincasTot]\n",
        "    print(f'Número de Clusters: {K1}, \\t Capacidad Q: {Q}')\n",
        "    f.write(f'Número de Clusters: {K1}, \\t Capacidad Q: {Q}')\n",
        "    f.write('\\n')\n",
        "    try:\n",
        "      print(dictFOTotalC1[K1, Q][0], round(sum(dictFOTotalC1[K1, Q][0]), 2))\n",
        "      print('\\n')\n",
        "      f.write(f'{dictFOTotalC1[K1, Q][0]}, {round(sum(dictFOTotalC1[K1, Q][0]), 2)}')\n",
        "      f.write('\\n')\n",
        "      f.write('\\n')\n",
        "    except:\n",
        "      print(dictFOTotalC1[K1, Q][0])\n",
        "      print('\\n')\n",
        "      f.write(f'{dictFOTotalC1[K1, Q][0]}')\n",
        "      f.write('\\n')\n",
        "      f.write('\\n')\n",
        "\n",
        "for llave in list(dictFOTotalC1.keys()):\n",
        "  try:\n",
        "    f.write(f'{dictFOTotalC1[llave][1]}\\t{sum(dictFOTotalC1[llave][0])}')\n",
        "    f.write('\\n')\n",
        "  except:\n",
        "    f.write(f'{dictFOTotalC1[llave][1]}\\t{dictFOTotalC1[llave][0]}')\n",
        "    f.write('\\n')\n",
        "f.close()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUKzZFqImYQt",
        "outputId": "794335fd-db6c-4333-a006-c0bb641d0878"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acabó PAM iteración 1\n",
            "Valores F.O por cada Cluster de la iteración 1:\n",
            "\t FO cluster 0: 851.85,  Número de Vehículos: 8\n",
            "\t FO cluster 1: 505.99,  Número de Vehículos: 6\n",
            "\t FO cluster 2: 683.7,  Número de Vehículos: 8\n",
            "\t FO cluster 3: 805.65,  Número de Vehículos: 7\n",
            "\t FO cluster 4: 633.52,  Número de Vehículos: 7\n",
            "Número de Clusters: 5, \t Capacidad Q: 16\n",
            "[851.85, 505.99, 683.7, 805.65, 633.52] 3480.71\n",
            "\n",
            "\n",
            "Acabó PAM iteración 2\n",
            "Valores F.O por cada Cluster de la iteración 2:\n",
            "\t FO cluster 0: 498.76,  Número de Vehículos: 4\n",
            "\t FO cluster 1: 329.33,  Número de Vehículos: 4\n",
            "\t FO cluster 2: 381.66,  Número de Vehículos: 4\n",
            "\t FO cluster 3: 523.01,  Número de Vehículos: 5\n",
            "\t FO cluster 4: 437.34,  Número de Vehículos: 5\n",
            "Número de Clusters: 5, \t Capacidad Q: 28\n",
            "[498.76, 329.33, 381.66, 523.01, 437.34] 2170.1\n",
            "\n",
            "\n",
            "Acabó PAM iteración 3\n",
            "Valores F.O por cada Cluster de la iteración 3:\n",
            "\t FO cluster 0: 398.14,  Número de Vehículos: 3\n",
            "\t FO cluster 1: 251.35,  Número de Vehículos: 3\n",
            "\t FO cluster 2: 292.32,  Número de Vehículos: 3\n",
            "\t FO cluster 3: 348.83,  Número de Vehículos: 3\n",
            "\t FO cluster 4: 317.1,  Número de Vehículos: 3\n",
            "Número de Clusters: 5, \t Capacidad Q: 40\n",
            "[398.14, 251.35, 292.32, 348.83, 317.1] 1607.74\n",
            "\n",
            "\n",
            "Acabó PAM iteración 4\n",
            "Valores F.O por cada Cluster de la iteración 4:\n",
            "\t FO cluster 0: 258.02,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 393.24,  Número de Vehículos: 3\n",
            "\t FO cluster 2: 188.5,  Número de Vehículos: 2\n",
            "\t FO cluster 3: 269.48,  Número de Vehículos: 3\n",
            "\t FO cluster 4: 334.17,  Número de Vehículos: 3\n",
            "Número de Clusters: 5, \t Capacidad Q: 52\n",
            "[258.02, 393.24, 188.5, 269.48, 334.17] 1443.41\n",
            "\n",
            "\n",
            "Acabó PAM iteración 5\n",
            "Valores F.O por cada Cluster de la iteración 5:\n",
            "\t FO cluster 0: 406.0,  Número de Vehículos: 4\n",
            "\t FO cluster 1: Modelo Infactible\n",
            "\t FO cluster 2: Modelo Infactible\n",
            "\t FO cluster 3: Modelo Infactible\n",
            "\t FO cluster 4: Modelo Infactible\n",
            "\t FO cluster 5: Modelo Infactible\n",
            "\t FO cluster 6: Modelo Infactible\n",
            "\t FO cluster 7: Modelo Infactible\n",
            "\t FO cluster 8: Modelo Infactible\n",
            "\t FO cluster 9: Modelo Infactible\n",
            "Número de Clusters: 10, \t Capacidad Q: 16\n",
            "Infactible\n",
            "\n",
            "\n",
            "Acabó PAM iteración 6\n",
            "Valores F.O por cada Cluster de la iteración 6:\n",
            "\t FO cluster 0: 202.96,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 320.14,  Número de Vehículos: 3\n",
            "\t FO cluster 2: 274.93,  Número de Vehículos: 2\n",
            "\t FO cluster 3: 259.62,  Número de Vehículos: 2\n",
            "\t FO cluster 4: 184.17,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 240.19,  Número de Vehículos: 3\n",
            "\t FO cluster 6: 212.12,  Número de Vehículos: 3\n",
            "\t FO cluster 7: 327.35,  Número de Vehículos: 3\n",
            "\t FO cluster 8: 229.49,  Número de Vehículos: 2\n",
            "\t FO cluster 9: 230.41,  Número de Vehículos: 2\n",
            "Número de Clusters: 10, \t Capacidad Q: 28\n",
            "[202.96, 320.14, 274.93, 259.62, 184.17, 240.19, 212.12, 327.35, 229.49, 230.41] 2481.38\n",
            "\n",
            "\n",
            "Acabó PAM iteración 7\n",
            "Valores F.O por cada Cluster de la iteración 7:\n",
            "\t FO cluster 0: 228.84,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 220.46,  Número de Vehículos: 2\n",
            "\t FO cluster 2: 252.32,  Número de Vehículos: 2\n",
            "\t FO cluster 3: 175.6,  Número de Vehículos: 2\n",
            "\t FO cluster 4: 203.71,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 181.38,  Número de Vehículos: 2\n",
            "\t FO cluster 6: 142.68,  Número de Vehículos: 2\n",
            "\t FO cluster 7: 238.75,  Número de Vehículos: 2\n",
            "\t FO cluster 8: 252.01,  Número de Vehículos: 2\n",
            "\t FO cluster 9: 266.41,  Número de Vehículos: 2\n",
            "Número de Clusters: 10, \t Capacidad Q: 40\n",
            "[228.84, 220.46, 252.32, 175.6, 203.71, 181.38, 142.68, 238.75, 252.01, 266.41] 2162.16\n",
            "\n",
            "\n",
            "Acabó PAM iteración 8\n",
            "Valores F.O por cada Cluster de la iteración 8:\n",
            "\t FO cluster 0: 162.06,  Número de Vehículos: 1\n",
            "\t FO cluster 1: 220.48,  Número de Vehículos: 2\n",
            "\t FO cluster 2: 250.27,  Número de Vehículos: 2\n",
            "\t FO cluster 3: 164.11,  Número de Vehículos: 2\n",
            "\t FO cluster 4: 227.22,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 147.32,  Número de Vehículos: 2\n",
            "\t FO cluster 6: 132.75,  Número de Vehículos: 1\n",
            "\t FO cluster 7: 171.48,  Número de Vehículos: 1\n",
            "\t FO cluster 8: 166.89,  Número de Vehículos: 1\n",
            "\t FO cluster 9: 110.6,  Número de Vehículos: 1\n",
            "Número de Clusters: 10, \t Capacidad Q: 52\n",
            "[162.06, 220.48, 250.27, 164.11, 227.22, 147.32, 132.75, 171.48, 166.89, 110.6] 1753.18\n",
            "\n",
            "\n",
            "Acabó PAM iteración 9\n",
            "Valores F.O por cada Cluster de la iteración 9:\n",
            "\t FO cluster 0: 260.5,  Número de Vehículos: 3\n",
            "\t FO cluster 1: 321.03,  Número de Vehículos: 3\n",
            "\t FO cluster 2: 391.72,  Número de Vehículos: 3\n",
            "\t FO cluster 3: 326.54,  Número de Vehículos: 4\n",
            "\t FO cluster 4: 220.57,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 356.04,  Número de Vehículos: 3\n",
            "\t FO cluster 6: 236.43,  Número de Vehículos: 3\n",
            "\t FO cluster 7: 291.71,  Número de Vehículos: 3\n",
            "\t FO cluster 8: 285.2,  Número de Vehículos: 4\n",
            "\t FO cluster 9: 225.58,  Número de Vehículos: 2\n",
            "\t FO cluster 10: 217.02,  Número de Vehículos: 2\n",
            "\t FO cluster 11: 359.4,  Número de Vehículos: 3\n",
            "\t FO cluster 12: 352.59,  Número de Vehículos: 3\n",
            "\t FO cluster 13: 322.93,  Número de Vehículos: 3\n",
            "\t FO cluster 14: 306.05,  Número de Vehículos: 3\n",
            "Número de Clusters: 15, \t Capacidad Q: 16\n",
            "[260.5, 321.03, 391.72, 326.54, 220.57, 356.04, 236.43, 291.71, 285.2, 225.58, 217.02, 359.4, 352.59, 322.93, 306.05] 4473.31\n",
            "\n",
            "\n",
            "Acabó PAM iteración 10\n",
            "Valores F.O por cada Cluster de la iteración 10:\n",
            "\t FO cluster 0: 239.52,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 199.32,  Número de Vehículos: 2\n",
            "\t FO cluster 2: 222.4,  Número de Vehículos: 2\n",
            "\t FO cluster 3: 174.0,  Número de Vehículos: 2\n",
            "\t FO cluster 4: 224.94,  Número de Vehículos: 3\n",
            "\t FO cluster 5: 227.71,  Número de Vehículos: 2\n",
            "\t FO cluster 6: 160.13,  Número de Vehículos: 2\n",
            "\t FO cluster 7: 286.12,  Número de Vehículos: 2\n",
            "\t FO cluster 8: 184.71,  Número de Vehículos: 2\n",
            "\t FO cluster 9: 236.6,  Número de Vehículos: 2\n",
            "\t FO cluster 10: 154.71,  Número de Vehículos: 2\n",
            "\t FO cluster 11: 219.89,  Número de Vehículos: 2\n",
            "\t FO cluster 12: 223.17,  Número de Vehículos: 2\n",
            "\t FO cluster 13: 194.2,  Número de Vehículos: 2\n",
            "\t FO cluster 14: 221.26,  Número de Vehículos: 2\n",
            "Número de Clusters: 15, \t Capacidad Q: 28\n",
            "[239.52, 199.32, 222.4, 174.0, 224.94, 227.71, 160.13, 286.12, 184.71, 236.6, 154.71, 219.89, 223.17, 194.2, 221.26] 3168.68\n",
            "\n",
            "\n",
            "Acabó PAM iteración 11\n",
            "Valores F.O por cada Cluster de la iteración 11:\n",
            "\t FO cluster 0: 230.21,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 172.96,  Número de Vehículos: 1\n",
            "\t FO cluster 2: 135.98,  Número de Vehículos: 1\n",
            "\t FO cluster 3: 134.84,  Número de Vehículos: 2\n",
            "\t FO cluster 4: 152.38,  Número de Vehículos: 1\n",
            "\t FO cluster 5: 120.75,  Número de Vehículos: 1\n",
            "\t FO cluster 6: 137.79,  Número de Vehículos: 1\n",
            "\t FO cluster 7: 175.01,  Número de Vehículos: 1\n",
            "\t FO cluster 8: 201.0,  Número de Vehículos: 2\n",
            "\t FO cluster 9: 161.52,  Número de Vehículos: 1\n",
            "\t FO cluster 10: 103.41,  Número de Vehículos: 1\n",
            "\t FO cluster 11: 105.29,  Número de Vehículos: 1\n",
            "\t FO cluster 12: 213.07,  Número de Vehículos: 2\n",
            "\t FO cluster 13: 163.46,  Número de Vehículos: 2\n",
            "\t FO cluster 14: 148.88,  Número de Vehículos: 1\n",
            "Número de Clusters: 15, \t Capacidad Q: 40\n",
            "[230.21, 172.96, 135.98, 134.84, 152.38, 120.75, 137.79, 175.01, 201.0, 161.52, 103.41, 105.29, 213.07, 163.46, 148.88] 2356.55\n",
            "\n",
            "\n",
            "Acabó PAM iteración 12\n",
            "Valores F.O por cada Cluster de la iteración 12:\n",
            "\t FO cluster 0: 116.91,  Número de Vehículos: 1\n",
            "\t FO cluster 1: 131.2,  Número de Vehículos: 1\n",
            "\t FO cluster 2: 147.95,  Número de Vehículos: 1\n",
            "\t FO cluster 3: 143.05,  Número de Vehículos: 1\n",
            "\t FO cluster 4: 153.31,  Número de Vehículos: 1\n",
            "\t FO cluster 5: 170.7,  Número de Vehículos: 1\n",
            "\t FO cluster 6: 151.13,  Número de Vehículos: 1\n",
            "\t FO cluster 7: 136.89,  Número de Vehículos: 1\n",
            "\t FO cluster 8: 123.64,  Número de Vehículos: 1\n",
            "\t FO cluster 9: 112.05,  Número de Vehículos: 1\n",
            "\t FO cluster 10: 158.32,  Número de Vehículos: 2\n",
            "\t FO cluster 11: 103.41,  Número de Vehículos: 1\n",
            "\t FO cluster 12: 137.85,  Número de Vehículos: 2\n",
            "\t FO cluster 13: 151.05,  Número de Vehículos: 1\n",
            "\t FO cluster 14: 162.84,  Número de Vehículos: 1\n",
            "Número de Clusters: 15, \t Capacidad Q: 52\n",
            "[116.91, 131.2, 147.95, 143.05, 153.31, 170.7, 151.13, 136.89, 123.64, 112.05, 158.32, 103.41, 137.85, 151.05, 162.84] 2100.3\n",
            "\n",
            "\n",
            "Acabó PAM iteración 13\n",
            "Valores F.O por cada Cluster de la iteración 13:\n",
            "\t FO cluster 0: 305.16,  Número de Vehículos: 3\n",
            "\t FO cluster 1: 197.6,  Número de Vehículos: 2\n",
            "\t FO cluster 2: 220.84,  Número de Vehículos: 2\n",
            "\t FO cluster 3: 283.77,  Número de Vehículos: 3\n",
            "\t FO cluster 4: 257.51,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 190.68,  Número de Vehículos: 2\n",
            "\t FO cluster 6: 266.64,  Número de Vehículos: 2\n",
            "\t FO cluster 7: 321.93,  Número de Vehículos: 3\n",
            "\t FO cluster 8: 237.86,  Número de Vehículos: 2\n",
            "\t FO cluster 9: 177.01,  Número de Vehículos: 2\n",
            "\t FO cluster 10: 160.3,  Número de Vehículos: 2\n",
            "\t FO cluster 11: 192.94,  Número de Vehículos: 2\n",
            "\t FO cluster 12: 169.98,  Número de Vehículos: 2\n",
            "\t FO cluster 13: 192.65,  Número de Vehículos: 2\n",
            "\t FO cluster 14: 194.34,  Número de Vehículos: 2\n",
            "\t FO cluster 15: 181.12,  Número de Vehículos: 2\n",
            "\t FO cluster 16: 278.74,  Número de Vehículos: 3\n",
            "\t FO cluster 17: 229.84,  Número de Vehículos: 3\n",
            "\t FO cluster 18: 181.9,  Número de Vehículos: 3\n",
            "\t FO cluster 19: 287.69,  Número de Vehículos: 3\n",
            "Número de Clusters: 20, \t Capacidad Q: 16\n",
            "[305.16, 197.6, 220.84, 283.77, 257.51, 190.68, 266.64, 321.93, 237.86, 177.01, 160.3, 192.94, 169.98, 192.65, 194.34, 181.12, 278.74, 229.84, 181.9, 287.69] 4528.5\n",
            "\n",
            "\n",
            "Acabó PAM iteración 14\n",
            "Valores F.O por cada Cluster de la iteración 14:\n",
            "\t FO cluster 0: 202.96,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 221.93,  Número de Vehículos: 2\n",
            "\t FO cluster 2: 97.66,  Número de Vehículos: 1\n",
            "\t FO cluster 3: 109.1,  Número de Vehículos: 1\n",
            "\t FO cluster 4: 172.59,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 157.19,  Número de Vehículos: 2\n",
            "\t FO cluster 6: 257.51,  Número de Vehículos: 2\n",
            "\t FO cluster 7: 201.26,  Número de Vehículos: 2\n",
            "\t FO cluster 8: 200.31,  Número de Vehículos: 2\n",
            "\t FO cluster 9: 125.5,  Número de Vehículos: 2\n",
            "\t FO cluster 10: 95.68,  Número de Vehículos: 1\n",
            "\t FO cluster 11: 216.84,  Número de Vehículos: 2\n",
            "\t FO cluster 12: 194.87,  Número de Vehículos: 2\n",
            "\t FO cluster 13: 99.71,  Número de Vehículos: 1\n",
            "\t FO cluster 14: 162.26,  Número de Vehículos: 1\n",
            "\t FO cluster 15: 137.78,  Número de Vehículos: 1\n",
            "\t FO cluster 16: 131.22,  Número de Vehículos: 1\n",
            "\t FO cluster 17: 223.6,  Número de Vehículos: 2\n",
            "\t FO cluster 18: 104.75,  Número de Vehículos: 1\n",
            "\t FO cluster 19: 111.44,  Número de Vehículos: 1\n",
            "Número de Clusters: 20, \t Capacidad Q: 28\n",
            "[202.96, 221.93, 97.66, 109.1, 172.59, 157.19, 257.51, 201.26, 200.31, 125.5, 95.68, 216.84, 194.87, 99.71, 162.26, 137.78, 131.22, 223.6, 104.75, 111.44] 3224.16\n",
            "\n",
            "\n",
            "Acabó PAM iteración 15\n",
            "Valores F.O por cada Cluster de la iteración 15:\n",
            "\t FO cluster 0: 150.82,  Número de Vehículos: 2\n",
            "\t FO cluster 1: 158.46,  Número de Vehículos: 1\n",
            "\t FO cluster 2: 169.93,  Número de Vehículos: 1\n",
            "\t FO cluster 3: 142.87,  Número de Vehículos: 1\n",
            "\t FO cluster 4: 129.86,  Número de Vehículos: 2\n",
            "\t FO cluster 5: 143.24,  Número de Vehículos: 1\n",
            "\t FO cluster 6: 109.1,  Número de Vehículos: 1\n",
            "\t FO cluster 7: 93.99,  Número de Vehículos: 1\n",
            "\t FO cluster 8: 99.71,  Número de Vehículos: 1\n",
            "\t FO cluster 9: 139.31,  Número de Vehículos: 1\n",
            "\t FO cluster 10: 162.26,  Número de Vehículos: 1\n",
            "\t FO cluster 11: 103.8,  Número de Vehículos: 1\n",
            "\t FO cluster 12: 114.04,  Número de Vehículos: 1\n",
            "\t FO cluster 13: 131.22,  Número de Vehículos: 1\n",
            "\t FO cluster 14: 148.52,  Número de Vehículos: 1\n",
            "\t FO cluster 15: 203.26,  Número de Vehículos: 2\n",
            "\t FO cluster 16: 136.71,  Número de Vehículos: 1\n",
            "\t FO cluster 17: 104.44,  Número de Vehículos: 1\n",
            "\t FO cluster 18: 132.94,  Número de Vehículos: 1\n",
            "\t FO cluster 19: 155.48,  Número de Vehículos: 1\n",
            "Número de Clusters: 20, \t Capacidad Q: 40\n",
            "[150.82, 158.46, 169.93, 142.87, 129.86, 143.24, 109.1, 93.99, 99.71, 139.31, 162.26, 103.8, 114.04, 131.22, 148.52, 203.26, 136.71, 104.44, 132.94, 155.48] 2729.96\n",
            "\n",
            "\n",
            "Acabó PAM iteración 16\n",
            "Valores F.O por cada Cluster de la iteración 16:\n",
            "\t FO cluster 0: 93.99,  Número de Vehículos: 1\n",
            "\t FO cluster 1: 162.26,  Número de Vehículos: 1\n",
            "\t FO cluster 2: 159.86,  Número de Vehículos: 1\n",
            "\t FO cluster 3: 119.78,  Número de Vehículos: 1\n",
            "\t FO cluster 4: 116.56,  Número de Vehículos: 1\n",
            "\t FO cluster 5: 104.44,  Número de Vehículos: 1\n",
            "\t FO cluster 6: 102.33,  Número de Vehículos: 1\n",
            "\t FO cluster 7: 131.22,  Número de Vehículos: 1\n",
            "\t FO cluster 8: 114.78,  Número de Vehículos: 1\n",
            "\t FO cluster 9: 132.94,  Número de Vehículos: 1\n",
            "\t FO cluster 10: 83.66,  Número de Vehículos: 1\n",
            "\t FO cluster 11: 148.3,  Número de Vehículos: 1\n",
            "\t FO cluster 12: 105.24,  Número de Vehículos: 1\n",
            "\t FO cluster 13: 148.52,  Número de Vehículos: 1\n",
            "\t FO cluster 14: 136.71,  Número de Vehículos: 1\n",
            "\t FO cluster 15: 137.12,  Número de Vehículos: 1\n",
            "\t FO cluster 16: 102.3,  Número de Vehículos: 1\n",
            "\t FO cluster 17: 109.1,  Número de Vehículos: 1\n",
            "\t FO cluster 18: 143.24,  Número de Vehículos: 1\n",
            "\t FO cluster 19: 136.47,  Número de Vehículos: 1\n",
            "Número de Clusters: 20, \t Capacidad Q: 52\n",
            "[93.99, 162.26, 159.86, 119.78, 116.56, 104.44, 102.33, 131.22, 114.78, 132.94, 83.66, 148.3, 105.24, 148.52, 136.71, 137.12, 102.3, 109.1, 143.24, 136.47] 2488.82\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}