VRPPDTW tabu search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vehicule Rooting Problem Time window with Pick-up and Delivery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В дальнейшем мы представим параметры, определенные в нашей модели на языке Python.\n",
    "\n",
    "$t_{𝑖j𝑘}$: Временная матрица, которая определяет время в пути от точки $i$ до точки $j$ на транспортном средстве $k$ представлены в data['tijk']\n",
    "\n",
    "$P= {1,2,3,…. ,n}$ - это набор узлов сбора представлены в data['P'].\n",
    "\n",
    "$D= {n+1,n+2,…, 2n}$ - это набор узлов доставкипредставлены в data['D'].\n",
    "\n",
    "$N$ определяется как $P \\cup D$\n",
    "\n",
    "Для удобства мы также определили data['pickups_deliveries'] для простого и понятного заполнения данных  наборов $P$ и $D$.\n",
    "\n",
    "$K$ - набор транспортных средств от $1$ до $k$ Мы определим количество транспортных средств data['k'] \n",
    "\n",
    "Что касается ограничения временного окна, мы будем предполагать, что обслуживание в точке $i$ должно быть выполнено между моментами ai и bi определяется data['time_windows']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random as rd\n",
    "import networkx as nx #to plt the graph\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "data = {}\n",
    "data['tijk'] = [ #tijk\n",
    "    [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],                 #0\n",
    "    [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],             #1 \n",
    "    [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],           #2\n",
    "    [8, 3, 11, 0, 1, 7, 10, 6, 10, 10, 14, 6, 7, 9, 14, 6, 16],          #3   \n",
    "    [7, 2, 10, 1, 0, 6, 9, 4, 8, 9, 13, 4, 6, 8, 12, 8, 14],             #4\n",
    "    [3, 6, 6, 7, 6, 0, 2, 3, 2, 2, 7, 9, 7, 7, 6, 12, 8],                #5\n",
    "    [6, 8, 3, 10, 9, 2, 0, 6, 2, 5, 4, 12, 10, 10, 6, 15, 5],            #6\n",
    "    [2, 4, 9, 6, 4, 3, 6, 0, 4, 4, 8, 5, 4, 3, 7, 8, 10],                #7\n",
    "    [3, 8, 5, 10, 8, 2, 2, 4, 0, 3, 4, 9, 8, 7, 3, 13, 6],               #8\n",
    "    [2, 8, 8, 10, 9, 2, 5, 4, 3, 0, 4, 6, 5, 4, 3, 9, 5],                #9\n",
    "    [6, 13, 4, 14, 13, 7, 4, 8, 4, 4, 0, 10, 9, 8, 4, 13, 4],            #10\n",
    "    [6, 7, 15, 6, 4, 9, 12, 5, 9, 6, 10, 0, 1, 3, 7, 3, 10],             #11\n",
    "    [4, 5, 14, 7, 6, 7, 10, 4, 8, 5, 9, 1, 0, 2, 6, 4, 8],               #12\n",
    "    [4, 8, 13, 9, 8, 7, 10, 3, 7, 4, 8, 3, 2, 0, 4, 5, 6],               #13\n",
    "    [5, 12, 9, 14, 12, 6, 6, 7, 3, 3, 4, 7, 6, 4, 0, 9, 2],              #14\n",
    "    [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],           #15\n",
    "    [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],            #16\n",
    "]\n",
    "data['time_windows'] = [\n",
    "   #(ai, bi)\n",
    "    (0, 100),  # i = o(k)\n",
    "    (0, 100),  # i = 1\n",
    "    (0, 100),  # i = 2\n",
    "    (0, 100),  # i = 3\n",
    "    (0, 100),  # i = 4\n",
    "    (0, 100),  # i = 5\n",
    "    (0, 100),  # i = 6\n",
    "    (0, 100),  # i = 7\n",
    "    (0, 100),  # i = 8\n",
    "    (0, 100),  # i = 9\n",
    "    (0, 100),  # i = 10\n",
    "    (0, 100),  # i = 11\n",
    "    (0, 100),  # i = 12\n",
    "    (0, 100),  # i = 13\n",
    "    (0, 100),  # i = 14\n",
    "    (0, 100),  # i = 15\n",
    "    (0, 100),  # i = 16\n",
    "]\n",
    "data['pickups_deliveries'] = [\n",
    "    [1, 6],\n",
    "    [2, 10],\n",
    "    [4, 3],\n",
    "    [5, 9],\n",
    "    [7, 8],\n",
    "    [15, 11],\n",
    "    [13, 12],\n",
    "    [16, 14],\n",
    "]\n",
    "data['k'] = 7  #k: num vehicule \n",
    "data['o(k)'] = 0 # o(k) origin\n",
    "\n",
    "data['P'] = []\n",
    "for j in range(len(data['pickups_deliveries'])):\n",
    "    data['P'].append(data['pickups_deliveries'][j][0])\n",
    "\n",
    "data['D'] = []\n",
    "for j in range(len(data['pickups_deliveries'])):\n",
    "    data['D'].append(data['pickups_deliveries'][j][1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы печатаем параметры, определенные выше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tijk = [[0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7], [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14], [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9], [8, 3, 11, 0, 1, 7, 10, 6, 10, 10, 14, 6, 7, 9, 14, 6, 16], [7, 2, 10, 1, 0, 6, 9, 4, 8, 9, 13, 4, 6, 8, 12, 8, 14], [3, 6, 6, 7, 6, 0, 2, 3, 2, 2, 7, 9, 7, 7, 6, 12, 8], [6, 8, 3, 10, 9, 2, 0, 6, 2, 5, 4, 12, 10, 10, 6, 15, 5], [2, 4, 9, 6, 4, 3, 6, 0, 4, 4, 8, 5, 4, 3, 7, 8, 10], [3, 8, 5, 10, 8, 2, 2, 4, 0, 3, 4, 9, 8, 7, 3, 13, 6], [2, 8, 8, 10, 9, 2, 5, 4, 3, 0, 4, 6, 5, 4, 3, 9, 5], [6, 13, 4, 14, 13, 7, 4, 8, 4, 4, 0, 10, 9, 8, 4, 13, 4], [6, 7, 15, 6, 4, 9, 12, 5, 9, 6, 10, 0, 1, 3, 7, 3, 10], [4, 5, 14, 7, 6, 7, 10, 4, 8, 5, 9, 1, 0, 2, 6, 4, 8], [4, 8, 13, 9, 8, 7, 10, 3, 7, 4, 8, 3, 2, 0, 4, 5, 6], [5, 12, 9, 14, 12, 6, 6, 7, 3, 3, 4, 7, 6, 4, 0, 9, 2], [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9], [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0]] \n",
      "\n",
      "P = [1, 2, 4, 5, 7, 15, 13, 16] \n",
      "\n",
      "D = [6, 10, 3, 9, 8, 11, 12, 14] \n",
      "\n",
      "N = [1, 2, 4, 5, 7, 15, 13, 16, 6, 10, 3, 9, 8, 11, 12, 14] \n",
      "\n",
      "k = 7 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"tijk =\", data['tijk'], '\\n')\n",
    "print(\"P =\", data['P'], '\\n')\n",
    "print(\"D =\", data['D'], '\\n')\n",
    "print(\"N =\", data['P'] + data['D'], '\\n')\n",
    "print(\"k =\", data['k'], '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Алгоритм поиска Табу\n",
    "Алгоритм поиска табу ближайшего соседа работает таким образом. Это метаэвристический метод: итеративно выстраивает путь, который заканчивается приемлемым решением. Действительно, мы отказываемся пройти через пункт, который уже был посещен\n",
    "\n",
    "Поэтому алгоритм выглядит следующим образом:\n",
    "\n",
    "Input Solution=[отправная_точка_автомобиля_к], tabu_list=[]\n",
    "================================\n",
    "    while not Все точки посещенные:\n",
    "        for каждую машину к:\n",
    "            если условия указанных в моделях ограничений выполнены, все:\n",
    "                Интенсификация: Расчет расстояний между текущим w = Решение [-1]\n",
    "                 и все точки, не принадлежащие tabu_list\n",
    "    \n",
    "                Диверсификация: v = min (d (w, v) для v не в Табу)\n",
    "    \n",
    "         Решение для автомобиля к = Решение + [v]\n",
    "         Добавление v в список Табу\n",
    "end while\n",
    "================================\n",
    "Output Решение $G$, как представлено в модели, является графом, назначенным автомобилю $k$ для всех автомобилей $K$ \n",
    "и $Tik$ время в точке $i$ для автомобиля $k$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tabu_search(data):\n",
    "    tabu_list = [int(name) for name in range(len(data['tijk']))]\n",
    "    G = [[0] for c in range(data['k'])]\n",
    "    Tik = np.zeros(data['k'])\n",
    "    tabu_list.remove(0)\n",
    "    p = np.zeros(data['k'])\n",
    "    while len(tabu_list) > 0:\n",
    "        for k in range(data['k']):\n",
    "            if len(tabu_list) == 0:\n",
    "                break\n",
    "            u = G[k][-1]\n",
    "            v = tabu_list[-1]\n",
    "            minimum = float(\"inf\")\n",
    "            for w in tabu_list:\n",
    "                for itr in range(50000):\n",
    "                    if data['tijk'][int(u)][int(w)] < minimum \\\n",
    "                        and w in data['P'] or (w in data['D'] \\\n",
    "                        and data['P'][data['D'].index(w)] not in tabu_list \\\n",
    "                        and data['P'][data['D'].index(w)] in G[k]): \n",
    "                        minimum = data['tijk'][int(u)][int(w)]\n",
    "                        v = w\n",
    "                    if Tik[k] + data['tijk'][int(u)][int(w)] > data['time_windows'][v][1]:\n",
    "                        break\n",
    "                        return('Error time window')                \n",
    "            G[k].append(v)\n",
    "            pk = int(p[k])\n",
    "            Tik[k] += data['tijk'][pk][v]\n",
    "            print('vehicule', k + 1)\n",
    "            print('ftom', int(p[k]),'to', v)\n",
    "            print('T {} {} = {}' .format(v, k + 1, Tik[k]))\n",
    "            print('\\n')\n",
    "            p[k] = v\n",
    "            tabu_list.remove(v)\n",
    "        if Tik[k] + data['tijk'][int(u)][int(v)] > data['time_windows'][v][1]:\n",
    "            raise ValueError('A very specific bad thing with time windows happened.')            \n",
    "    return G, Tik"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В дальнейшем мы видим отображение на каждой итерации запретного поиска:\n",
    "\n",
    "$T_{ik}$, переменная времени, указывающая дату начала обслуживания в точке $i$ на грузовике $k$.\n",
    "\n",
    "точка $j$, которую наш алгоритм выбрал в качестве пункта назначения для машины $k$, которая находится в точке $i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T_ik: Time variable indicating the start date of service at point i by the truck k.\n",
      "\n",
      "vehicule 1\n",
      "ftom 0 to 7\n",
      "T 7 1 = 2.0\n",
      "\n",
      "\n",
      "vehicule 2\n",
      "ftom 0 to 5\n",
      "T 5 2 = 3.0\n",
      "\n",
      "\n",
      "vehicule 3\n",
      "ftom 0 to 13\n",
      "T 13 3 = 4.0\n",
      "\n",
      "\n",
      "vehicule 4\n",
      "ftom 0 to 1\n",
      "T 1 4 = 6.0\n",
      "\n",
      "\n",
      "vehicule 5\n",
      "ftom 0 to 4\n",
      "T 4 5 = 7.0\n",
      "\n",
      "\n",
      "vehicule 6\n",
      "ftom 0 to 16\n",
      "T 16 6 = 7.0\n",
      "\n",
      "\n",
      "vehicule 7\n",
      "ftom 0 to 2\n",
      "T 2 7 = 9.0\n",
      "\n",
      "\n",
      "vehicule 1\n",
      "ftom 7 to 8\n",
      "T 8 1 = 6.0\n",
      "\n",
      "\n",
      "vehicule 2\n",
      "ftom 5 to 9\n",
      "T 9 2 = 5.0\n",
      "\n",
      "\n",
      "vehicule 3\n",
      "ftom 13 to 12\n",
      "T 12 3 = 6.0\n",
      "\n",
      "\n",
      "vehicule 4\n",
      "ftom 1 to 6\n",
      "T 6 4 = 14.0\n",
      "\n",
      "\n",
      "vehicule 5\n",
      "ftom 4 to 3\n",
      "T 3 5 = 8.0\n",
      "\n",
      "\n",
      "vehicule 6\n",
      "ftom 16 to 14\n",
      "T 14 6 = 9.0\n",
      "\n",
      "\n",
      "vehicule 7\n",
      "ftom 2 to 10\n",
      "T 10 7 = 13.0\n",
      "\n",
      "\n",
      "vehicule 1\n",
      "ftom 8 to 15\n",
      "T 15 1 = 19.0\n",
      "\n",
      "\n",
      "vehicule 2\n",
      "ftom 9 to 11\n",
      "T 11 2 = 11.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('T_ik: Time variable indicating the start date of service at point i by the truck k.\\n')\n",
    "G, Tik = tabu_search(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " графи $Gk = (V_K, A_k)$, представляющие маршрут каждого автомобиля $k$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G 1 = (V 1, A 1):\n",
      "[0, 7, 8, 15] \n",
      "\n",
      "G 2 = (V 2, A 2):\n",
      "[0, 5, 9, 11] \n",
      "\n",
      "G 3 = (V 3, A 3):\n",
      "[0, 13, 12] \n",
      "\n",
      "G 4 = (V 4, A 4):\n",
      "[0, 1, 6] \n",
      "\n",
      "G 5 = (V 5, A 5):\n",
      "[0, 4, 3] \n",
      "\n",
      "G 6 = (V 6, A 6):\n",
      "[0, 16, 14] \n",
      "\n",
      "G 7 = (V 7, A 7):\n",
      "[0, 2, 10] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for k in range(data['k']):\n",
    "    print('G {} = (V {}, A {}):'.format(k + 1, k + 1, k + 1))\n",
    "    print(G[k], '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы предлагаем визуализацию, полученную из результата для k = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visual(G, k):\n",
    "    g = nx.Graph() \n",
    "    for k in range (data['k']):  \n",
    "        for i in range(len(G[k]) - 1):\n",
    "            g.add_edge(G[k][i], G[k][i+1]) \n",
    "    nx.draw_circular(g, with_labels = True, edge_color='r') \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual(G, data['k'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}