results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_result(file_path):\n",
    "    results = []\n",
    "    with open(file_path, 'r') as f:\n",
    "        data = f.read()\n",
    "        for line in data.split(\"\\n\")[:-1]:\n",
    "            instance_name = line.split(\";\")[0].split(\":\")[0].split(\"/\")[-1].split(\"inst\")[1].split(\".\")[0]\n",
    "            runtime = float(line.split(\";\")[0].split(\":\")[-1].split(\"=\")[1].split(\"sec\")[0])\n",
    "            obj_value = int(line.split(\";\")[1].split(\"=\")[1])\n",
    "            result = [instance_name, runtime, obj_value]\n",
    "            if len(line.split(\";\")) == 3:\n",
    "                gap = float(line.split(\";\")[2].split(\"=\")[1].split(\"%\")[0])\n",
    "                result = [instance_name, runtime, obj_value, gap]\n",
    "            results.append(result)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Multi_I10_J50_T3_0', 0.0, 34509],\n",
       " ['Multi_I10_J50_T3_1', 0.0, 36641],\n",
       " ['Multi_I20_J1000_T8_0', 0.19, 731218],\n",
       " ['Multi_I20_J1000_T8_1', 0.2, 807798],\n",
       " ['Multi_I20_J100_T4_0', 0.01, 68701],\n",
       " ['Multi_I20_J100_T4_1', 0.01, 108776],\n",
       " ['Multi_I20_J100_T8_0', 0.02, 127679],\n",
       " ['Multi_I20_J100_T8_1', 0.02, 116535],\n",
       " ['Multi_I20_J250_T4_0', 0.01, 112459],\n",
       " ['Multi_I20_J250_T4_1', 0.02, 232129],\n",
       " ['Multi_I20_J250_T8_0', 0.05, 235372],\n",
       " ['Multi_I20_J250_T8_1', 0.05, 209595],\n",
       " ['Multi_I30_J1000_T12_0', 0.54, 1007963],\n",
       " ['Multi_I30_J1000_T12_1', 0.59, 1224115],\n",
       " ['Multi_I30_J1000_T8_0', 0.23, 716045],\n",
       " ['Multi_I30_J1000_T8_1', 0.29, 1181039],\n",
       " ['Multi_I30_J250_T12_0', 0.13, 299654],\n",
       " ['Multi_I30_J250_T12_1', 0.14, 276580],\n",
       " ['Multi_I30_J250_T4_0', 0.02, 266514],\n",
       " ['Multi_I30_J250_T4_1', 0.02, 322845],\n",
       " ['Multi_I30_J250_T8_0', 0.07, 348154],\n",
       " ['Multi_I30_J250_T8_1', 0.07, 360819],\n",
       " ['Multi_I30_J500_T12_0', 0.3, 624388],\n",
       " ['Multi_I30_J500_T12_1', 0.29, 625574],\n",
       " ['Multi_I30_J500_T8_0', 0.11, 395191],\n",
       " ['Multi_I30_J500_T8_1', 0.12, 400793],\n",
       " ['Multi_I50_J1000_T12_0', 0.83, 1583818],\n",
       " ['Multi_I50_J1000_T12_1', 0.86, 1555188],\n",
       " ['Multi_I50_J1000_T8_0', 0.37, 1206507],\n",
       " ['Multi_I50_J1000_T8_1', 0.42, 1789442],\n",
       " ['Multi_I50_J250_T12_0', 0.22, 497921],\n",
       " ['Multi_I50_J250_T12_1', 0.22, 550365],\n",
       " ['Multi_I50_J250_T8_0', 0.09, 371804],\n",
       " ['Multi_I50_J250_T8_1', 0.08, 271628],\n",
       " ['Multi_I50_J500_T12_0', 0.34, 529743],\n",
       " ['Multi_I50_J500_T12_1', 0.45, 875421],\n",
       " ['Multi_I50_J500_T8_0', 0.18, 625671],\n",
       " ['Multi_I50_J500_T8_1', 0.22, 961577],\n",
       " ['Single_I10_J50_T3_0', 0.0, 23484],\n",
       " ['Single_I10_J50_T3_1', 0.0, 24301],\n",
       " ['Single_I20_J1000_T8_0', 0.18, 548961],\n",
       " ['Single_I20_J1000_T8_1', 0.17, 529254],\n",
       " ['Single_I20_J100_T4_0', 0.01, 45097],\n",
       " ['Single_I20_J100_T4_1', 0.0, 44157],\n",
       " ['Single_I20_J100_T8_0', 0.02, 81883],\n",
       " ['Single_I20_J100_T8_1', 0.02, 80797],\n",
       " ['Single_I20_J250_T4_0', 0.01, 87633],\n",
       " ['Single_I20_J250_T4_1', 0.01, 90916],\n",
       " ['Single_I20_J250_T8_0', 0.04, 161142],\n",
       " ['Single_I20_J250_T8_1', 0.04, 156920],\n",
       " ['Single_I30_J1000_T12_0', 0.47, 776119],\n",
       " ['Single_I30_J1000_T12_1', 0.48, 793688],\n",
       " ['Single_I30_J1000_T8_0', 0.21, 535028],\n",
       " ['Single_I30_J1000_T8_1', 0.22, 525009],\n",
       " ['Single_I30_J250_T12_0', 0.13, 221491],\n",
       " ['Single_I30_J250_T12_1', 0.12, 229222],\n",
       " ['Single_I30_J250_T4_0', 0.01, 88461],\n",
       " ['Single_I30_J250_T4_1', 0.01, 86863],\n",
       " ['Single_I30_J250_T8_0', 0.05, 154812],\n",
       " ['Single_I30_J250_T8_1', 0.05, 158698],\n",
       " ['Single_I30_J500_T12_0', 0.24, 401471],\n",
       " ['Single_I30_J500_T12_1', 0.24, 413236],\n",
       " ['Single_I30_J500_T8_0', 0.11, 287171],\n",
       " ['Single_I30_J500_T8_1', 0.11, 288331],\n",
       " ['Single_I50_J1000_T12_0', 0.63, 773459],\n",
       " ['Single_I50_J1000_T12_1', 0.62, 767560],\n",
       " ['Single_I50_J1000_T8_0', 0.28, 512626],\n",
       " ['Single_I50_J1000_T8_1', 0.27, 532711],\n",
       " ['Single_I50_J250_T12_0', 0.16, 229611],\n",
       " ['Single_I50_J250_T12_1', 0.16, 228604],\n",
       " ['Single_I50_J250_T8_0', 0.07, 157450],\n",
       " ['Single_I50_J250_T8_1', 0.07, 160494],\n",
       " ['Single_I50_J500_T12_0', 0.31, 405883],\n",
       " ['Single_I50_J500_T12_1', 0.32, 409894],\n",
       " ['Single_I50_J500_T8_0', 0.15, 282693],\n",
       " ['Single_I50_J500_T8_1', 0.13, 284348]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heuristic_results = get_result(\"log/H/results_H_300_seconds.csv\")\n",
    "heuristic_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Multi_I10_J50_T3_0', 0.24, 34325, 0.0],\n",
       " ['Multi_I10_J50_T3_1', 0.14, 36519, 0.0],\n",
       " ['Multi_I20_J1000_T8_0', 269.39, 718670, 0.26],\n",
       " ['Multi_I20_J1000_T8_1', 284.8, 797646, 0.7156],\n",
       " ['Multi_I20_J100_T4_0', 0.51, 67925, 0.0],\n",
       " ['Multi_I20_J100_T4_1', 0.32, 108492, 0.0],\n",
       " ['Multi_I20_J100_T8_0', 5.89, 126472, 0.0],\n",
       " ['Multi_I20_J100_T8_1', 23.27, 113586, 0.0001],\n",
       " ['Multi_I20_J250_T4_0', 50.92, 110531, 0.0],\n",
       " ['Multi_I20_J250_T4_1', 2.44, 231099, 0.0001],\n",
       " ['Multi_I20_J250_T8_0', 300.02, 229872, 0.3498],\n",
       " ['Multi_I20_J250_T8_1', 300.03, 205247, 0.0429],\n",
       " ['Multi_I30_J1000_T12_0', 278.87, 1004033, 2.3426],\n",
       " ['Multi_I30_J1000_T12_1', 179.72, 1210809, 1.161],\n",
       " ['Multi_I30_J1000_T8_0', 265.64, 711226, 2.3899],\n",
       " ['Multi_I30_J1000_T8_1', 300.0, 1164861, 0.6358],\n",
       " ['Multi_I30_J250_T12_0', 300.03, 310252, 6.3297],\n",
       " ['Multi_I30_J250_T12_1', 300.02, 276620, 2.9958],\n",
       " ['Multi_I30_J250_T4_0', 9.26, 264690, 0.0],\n",
       " ['Multi_I30_J250_T4_1', 7.73, 319936, 0.0001],\n",
       " ['Multi_I30_J250_T8_0', 20.31, 344203, 0.0],\n",
       " ['Multi_I30_J250_T8_1', 9.26, 356601, 0.0],\n",
       " ['Multi_I30_J500_T12_0', 300.1, 620885, 1.214],\n",
       " ['Multi_I30_J500_T12_1', 291.39, 620104, 0.8258],\n",
       " ['Multi_I30_J500_T8_0', 280.42, 392735, 1.7681],\n",
       " ['Multi_I30_J500_T8_1', 300.02, 396201, 0.628],\n",
       " ['Multi_I50_J1000_T12_0', 239.02, 1576999, 1.4863],\n",
       " ['Multi_I50_J1000_T12_1', 294.76, 1544956, 1.1161],\n",
       " ['Multi_I50_J1000_T8_0', 201.37, 1194010, 0.0001],\n",
       " ['Multi_I50_J1000_T8_1', 300.0, 1770725, 0.0208],\n",
       " ['Multi_I50_J250_T12_0', 299.07, 519762, 5.6073],\n",
       " ['Multi_I50_J250_T12_1', 78.33, 542527, 0.0001],\n",
       " ['Multi_I50_J250_T8_0', 290.98, 370637, 1.2278],\n",
       " ['Multi_I50_J250_T8_1', 294.09, 269988, 0.2208],\n",
       " ['Multi_I50_J500_T12_0', 270.13, 530544, 3.908],\n",
       " ['Multi_I50_J500_T12_1', 300.0, 869707, 1.2728],\n",
       " ['Multi_I50_J500_T8_0', 180.99, 618087, 0.0],\n",
       " ['Multi_I50_J500_T8_1', 205.55, 953452, 0.0],\n",
       " ['Single_I10_J50_T3_0', 0.14, 23202, 0.0],\n",
       " ['Single_I10_J50_T3_1', 0.12, 24133, 0.0],\n",
       " ['Single_I20_J1000_T8_0', 290.56, 531134, 2.5539],\n",
       " ['Single_I20_J1000_T8_1', 283.29, 517860, 1.4129],\n",
       " ['Single_I20_J100_T4_0', 3.22, 44477, 0.0],\n",
       " ['Single_I20_J100_T4_1', 4.82, 43432, 0.0],\n",
       " ['Single_I20_J100_T8_0', 212.0, 80097, 0.0],\n",
       " ['Single_I20_J100_T8_1', 21.98, 79268, 0.0],\n",
       " ['Single_I20_J250_T4_0', 211.2, 86069, 0.0],\n",
       " ['Single_I20_J250_T4_1', 108.68, 88165, 0.0],\n",
       " ['Single_I20_J250_T8_0', 300.01, 156867, 1.7837],\n",
       " ['Single_I20_J250_T8_1', 300.2, 153778, 0.7908],\n",
       " ['Single_I30_J1000_T12_0', 300.01, 764049, 4.1657],\n",
       " ['Single_I30_J1000_T12_1', 300.25, 1021783, 100.0],\n",
       " ['Single_I30_J1000_T8_0', 209.58, 538790, 6.2273],\n",
       " ['Single_I30_J1000_T8_1', 252.8, 534269, 5.8073],\n",
       " ['Single_I30_J250_T12_0', 300.02, 220905, 2.99],\n",
       " ['Single_I30_J250_T12_1', 300.01, 231221, 5.6297],\n",
       " ['Single_I30_J250_T4_0', 299.81, 86451, 0.384],\n",
       " ['Single_I30_J250_T4_1', 300.08, 85118, 0.3853],\n",
       " ['Single_I30_J250_T8_0', 300.01, 151186, 1.1826],\n",
       " ['Single_I30_J250_T8_1', 300.26, 157002, 3.5586],\n",
       " ['Single_I30_J500_T12_0', 300.03, 412524, 6.8219],\n",
       " ['Single_I30_J500_T12_1', 164.44, 421898, 7.3909],\n",
       " ['Single_I30_J500_T8_0', 162.3, 293992, 7.1366],\n",
       " ['Single_I30_J500_T8_1', 300.01, 290472, 5.9482],\n",
       " ['Single_I50_J1000_T12_0', 221.91, 789867, 8.2809],\n",
       " ['Single_I50_J1000_T12_1', 277.72, 769770, 6.253],\n",
       " ['Single_I50_J1000_T8_0', 292.76, 510800, 4.4734],\n",
       " ['Single_I50_J1000_T8_1', 211.94, 541740, 7.3672],\n",
       " ['Single_I50_J250_T12_0', 300.01, 229406, 5.1808],\n",
       " ['Single_I50_J250_T12_1', 297.49, 228851, 5.2383],\n",
       " ['Single_I50_J250_T8_0', 218.86, 158055, 5.2627],\n",
       " ['Single_I50_J250_T8_1', 300.01, 161124, 5.9284],\n",
       " ['Single_I50_J500_T12_0', 262.13, 407385, 5.4096],\n",
       " ['Single_I50_J500_T12_1', 280.23, 421229, 8.6036],\n",
       " ['Single_I50_J500_T8_0', 286.67, 286869, 6.634],\n",
       " ['Single_I50_J500_T8_1', 194.12, 278307, 3.9518]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mip_results = get_result(\"log/MIP/results_MIP_300_seconds.csv\")\n",
    "mip_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Multi_I10_J50_T3_0', 0.0, 34509, 0.5332],\n",
       " ['Multi_I10_J50_T3_1', 0.0, 36641, 0.333],\n",
       " ['Multi_I20_J1000_T8_0', 0.19, 731218, 1.976],\n",
       " ['Multi_I20_J1000_T8_1', 0.2, 807798, 1.9723],\n",
       " ['Multi_I20_J100_T4_0', 0.01, 68701, 1.1295],\n",
       " ['Multi_I20_J100_T4_1', 0.01, 108776, 0.2611],\n",
       " ['Multi_I20_J100_T8_0', 0.02, 127679, 0.9453],\n",
       " ['Multi_I20_J100_T8_1', 0.02, 116535, 2.5307],\n",
       " ['Multi_I20_J250_T4_0', 0.01, 112459, 1.7144],\n",
       " ['Multi_I20_J250_T4_1', 0.02, 232129, 0.4438],\n",
       " ['Multi_I20_J250_T8_0', 0.05, 235372, 2.6865],\n",
       " ['Multi_I20_J250_T8_1', 0.05, 209595, 2.1174],\n",
       " ['Multi_I30_J1000_T12_0', 0.54, 1007963, 2.7325],\n",
       " ['Multi_I30_J1000_T12_1', 0.59, 1224115, 2.248],\n",
       " ['Multi_I30_J1000_T8_0', 0.23, 716045, 3.0629],\n",
       " ['Multi_I30_J1000_T8_1', 0.29, 1181039, 2.0056],\n",
       " ['Multi_I30_J250_T12_0', 0.13, 299654, 9.8664],\n",
       " ['Multi_I30_J250_T12_1', 0.14, 276580, 3.0103],\n",
       " ['Multi_I30_J250_T4_0', 0.02, 266514, 0.6844],\n",
       " ['Multi_I30_J250_T4_1', 0.02, 322845, 0.9012],\n",
       " ['Multi_I30_J250_T8_0', 0.07, 348154, 1.1348],\n",
       " ['Multi_I30_J250_T8_1', 0.07, 360819, 1.169],\n",
       " ['Multi_I30_J500_T12_0', 0.3, 624388, 1.775],\n",
       " ['Multi_I30_J500_T12_1', 0.29, 625574, 1.7002],\n",
       " ['Multi_I30_J500_T8_0', 0.11, 395191, 2.3896],\n",
       " ['Multi_I30_J500_T8_1', 0.12, 400793, 1.7737],\n",
       " ['Multi_I50_J1000_T12_0', 0.83, 1583818, 1.9168],\n",
       " ['Multi_I50_J1000_T12_1', 0.86, 1555188, 1.774],\n",
       " ['Multi_I50_J1000_T8_0', 0.37, 1206507, 1.0359],\n",
       " ['Multi_I50_J1000_T8_1', 0.42, 1789442, 1.0668],\n",
       " ['Multi_I50_J250_T12_0', 0.22, 497921, 9.9937],\n",
       " ['Multi_I50_J250_T12_1', 0.22, 550365, 1.4242],\n",
       " ['Multi_I50_J250_T8_0', 0.09, 371804, 1.5417],\n",
       " ['Multi_I50_J250_T8_1', 0.08, 271628, 0.8246],\n",
       " ['Multi_I50_J500_T12_0', 0.34, 529743, 4.0592],\n",
       " ['Multi_I50_J500_T12_1', 0.45, 875421, 1.9255],\n",
       " ['Multi_I50_J500_T8_0', 0.18, 625671, 1.2121],\n",
       " ['Multi_I50_J500_T8_1', 0.22, 961577, 0.845],\n",
       " ['Single_I10_J50_T3_0', 0.0, 23484, 1.2008],\n",
       " ['Single_I10_J50_T3_1', 0.0, 24301, 0.6913],\n",
       " ['Single_I20_J1000_T8_0', 0.18, 548961, 5.8013],\n",
       " ['Single_I20_J1000_T8_1', 0.17, 529254, 3.5657],\n",
       " ['Single_I20_J100_T4_0', 0.01, 45097, 1.3748],\n",
       " ['Single_I20_J100_T4_1', 0.0, 44157, 1.6419],\n",
       " ['Single_I20_J100_T8_0', 0.02, 81883, 2.1812],\n",
       " ['Single_I20_J100_T8_1', 0.02, 80797, 1.8924],\n",
       " ['Single_I20_J250_T4_0', 0.01, 87633, 1.7847],\n",
       " ['Single_I20_J250_T4_1', 0.01, 90916, 3.0259],\n",
       " ['Single_I20_J250_T8_0', 0.04, 161142, 4.4366],\n",
       " ['Single_I20_J250_T8_1', 0.04, 156920, 2.7931],\n",
       " ['Single_I30_J1000_T12_0', 0.47, 776119, 5.7209],\n",
       " ['Single_I30_J1000_T12_1', 0.48, 793688, 128.7386],\n",
       " ['Single_I30_J1000_T8_0', 0.21, 535028, 6.9304],\n",
       " ['Single_I30_J1000_T8_1', 0.22, 525009, 7.5711],\n",
       " ['Single_I30_J250_T12_0', 0.13, 221491, 3.2546],\n",
       " ['Single_I30_J250_T12_1', 0.12, 229222, 6.5018],\n",
       " ['Single_I30_J250_T4_0', 0.01, 88461, 2.6562],\n",
       " ['Single_I30_J250_T4_1', 0.01, 86863, 2.3942],\n",
       " ['Single_I30_J250_T8_0', 0.05, 154812, 3.5248],\n",
       " ['Single_I30_J250_T8_1', 0.05, 158698, 4.6273],\n",
       " ['Single_I30_J500_T12_0', 0.24, 401471, 9.575],\n",
       " ['Single_I30_J500_T12_1', 0.24, 413236, 9.487],\n",
       " ['Single_I30_J500_T8_0', 0.11, 287171, 9.5118],\n",
       " ['Single_I30_J500_T8_1', 0.11, 288331, 6.6907],\n",
       " ['Single_I50_J1000_T12_0', 0.63, 773459, 10.4023],\n",
       " ['Single_I50_J1000_T12_1', 0.62, 767560, 6.5409],\n",
       " ['Single_I50_J1000_T8_0', 0.28, 512626, 4.8296],\n",
       " ['Single_I50_J1000_T8_1', 0.27, 532711, 9.0621],\n",
       " ['Single_I50_J250_T12_0', 0.16, 229611, 5.2701],\n",
       " ['Single_I50_J250_T12_1', 0.16, 228604, 5.3463],\n",
       " ['Single_I50_J250_T8_0', 0.07, 157450, 5.6469],\n",
       " ['Single_I50_J250_T8_1', 0.07, 160494, 6.3209],\n",
       " ['Single_I50_J500_T12_0', 0.31, 405883, 5.7797],\n",
       " ['Single_I50_J500_T12_1', 0.32, 409894, 11.3689],\n",
       " ['Single_I50_J500_T8_0', 0.15, 282693, 8.1112],\n",
       " ['Single_I50_J500_T8_1', 0.13, 284348, 6.0763]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "def compute_heuristic_gap(heuristic_results, mip_results):\n",
    "    heuristic_gaps = []\n",
    "    if len(heuristic_results) != len(mip_results):\n",
    "        raise Exception(\"Heuristic and MIP results have different lengths\")\n",
    "    for i in range(len(heuristic_results)):\n",
    "        obj_h = heuristic_results[i][2]\n",
    "        obj_m = mip_results[i][2]\n",
    "        gap_m = mip_results[i][3]\n",
    "        heuristic_gaps.append(float(\"{:.4f}\".format(((abs(obj_m - obj_h) / obj_h) * 100) + gap_m)))\n",
    "\n",
    "    for i in range(len(heuristic_gaps)):\n",
    "        heuristic_results[i].append(heuristic_gaps[i])\n",
    "\n",
    "compute_heuristic_gap(heuristic_results, mip_results)\n",
    "heuristic_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# divide the instances into 2 sets, one for the \"Single\" and one for the \"Multi\" instances\n",
    "single_h_results = []\n",
    "multi_h_results = []\n",
    "single_mip_results = []\n",
    "multi_mip_results = []\n",
    "for result in heuristic_results:\n",
    "    if \"Single\" in result[0]:\n",
    "        single_h_results.append(result)\n",
    "    else:\n",
    "        multi_h_results.append(result)\n",
    "\n",
    "for result in mip_results:\n",
    "    if \"Single\" in result[0]:\n",
    "        single_mip_results.append(result)\n",
    "    else:\n",
    "        multi_mip_results.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Single_I10_J50_T3_0', 0.0, 23484, 1.2008],\n",
       " ['Single_I10_J50_T3_1', 0.0, 24301, 0.6913],\n",
       " ['Single_I20_J1000_T8_0', 0.18, 548961, 5.8013],\n",
       " ['Single_I20_J1000_T8_1', 0.17, 529254, 3.5657],\n",
       " ['Single_I20_J100_T4_0', 0.01, 45097, 1.3748],\n",
       " ['Single_I20_J100_T4_1', 0.0, 44157, 1.6419],\n",
       " ['Single_I20_J100_T8_0', 0.02, 81883, 2.1812],\n",
       " ['Single_I20_J100_T8_1', 0.02, 80797, 1.8924],\n",
       " ['Single_I20_J250_T4_0', 0.01, 87633, 1.7847],\n",
       " ['Single_I20_J250_T4_1', 0.01, 90916, 3.0259],\n",
       " ['Single_I20_J250_T8_0', 0.04, 161142, 4.4366],\n",
       " ['Single_I20_J250_T8_1', 0.04, 156920, 2.7931],\n",
       " ['Single_I30_J1000_T12_0', 0.47, 776119, 5.7209],\n",
       " ['Single_I30_J1000_T12_1', 0.48, 793688, 128.7386],\n",
       " ['Single_I30_J1000_T8_0', 0.21, 535028, 6.9304],\n",
       " ['Single_I30_J1000_T8_1', 0.22, 525009, 7.5711],\n",
       " ['Single_I30_J250_T12_0', 0.13, 221491, 3.2546],\n",
       " ['Single_I30_J250_T12_1', 0.12, 229222, 6.5018],\n",
       " ['Single_I30_J250_T4_0', 0.01, 88461, 2.6562],\n",
       " ['Single_I30_J250_T4_1', 0.01, 86863, 2.3942],\n",
       " ['Single_I30_J250_T8_0', 0.05, 154812, 3.5248],\n",
       " ['Single_I30_J250_T8_1', 0.05, 158698, 4.6273],\n",
       " ['Single_I30_J500_T12_0', 0.24, 401471, 9.575],\n",
       " ['Single_I30_J500_T12_1', 0.24, 413236, 9.487],\n",
       " ['Single_I30_J500_T8_0', 0.11, 287171, 9.5118],\n",
       " ['Single_I30_J500_T8_1', 0.11, 288331, 6.6907],\n",
       " ['Single_I50_J1000_T12_0', 0.63, 773459, 10.4023],\n",
       " ['Single_I50_J1000_T12_1', 0.62, 767560, 6.5409],\n",
       " ['Single_I50_J1000_T8_0', 0.28, 512626, 4.8296],\n",
       " ['Single_I50_J1000_T8_1', 0.27, 532711, 9.0621],\n",
       " ['Single_I50_J250_T12_0', 0.16, 229611, 5.2701],\n",
       " ['Single_I50_J250_T12_1', 0.16, 228604, 5.3463],\n",
       " ['Single_I50_J250_T8_0', 0.07, 157450, 5.6469],\n",
       " ['Single_I50_J250_T8_1', 0.07, 160494, 6.3209],\n",
       " ['Single_I50_J500_T12_0', 0.31, 405883, 5.7797],\n",
       " ['Single_I50_J500_T12_1', 0.32, 409894, 11.3689],\n",
       " ['Single_I50_J500_T8_0', 0.15, 282693, 8.1112],\n",
       " ['Single_I50_J500_T8_1', 0.13, 284348, 6.0763]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_h_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single Heuristic avg time:  0.1610526315789474\n",
      "Single MIP avg time:  228.14947368421053\n",
      "Multi Heuristic avg time:  0.2047368421052631\n",
      "Multi MIP avg time:  185.13236842105266\n"
     ]
    }
   ],
   "source": [
    "def compute_average(results, index):\n",
    "    total_time = 0\n",
    "    for result in results:\n",
    "        total_time += result[index]\n",
    "    return total_time / len(results)\n",
    "\n",
    "single_h_avg_time = compute_average(single_h_results, 1)\n",
    "single_mip_avg_time = compute_average(single_mip_results, 1)\n",
    "multi_h_avg_time = compute_average(multi_h_results, 1)\n",
    "multi_mip_avg_time = compute_average(multi_mip_results, 1)\n",
    "\n",
    "print(\"Single Heuristic avg time: \", single_h_avg_time)\n",
    "print(\"Single MIP avg time: \", single_mip_avg_time)\n",
    "print(\"Multi Heuristic avg time: \", multi_h_avg_time)\n",
    "print(\"Multi MIP avg time: \", multi_mip_avg_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single Heuristic avg gap:  8.48235\n",
      "Single MIP avg gap:  6.230344736842104\n",
      "Multi Heuristic avg gap:  2.071481578947369\n",
      "Multi MIP avg gap:  0.9610368421052629\n"
     ]
    }
   ],
   "source": [
    "single_h_avg_gap = compute_average(single_h_results, 3)\n",
    "single_mip_avg_gap = compute_average(single_mip_results, 3)\n",
    "multi_h_avg_gap = compute_average(multi_h_results, 3)\n",
    "multi_mip_avg_gap = compute_average(multi_mip_results, 3)\n",
    "print(\"Single Heuristic avg gap: \", single_h_avg_gap)\n",
    "print(\"Single MIP avg gap: \", single_mip_avg_gap)\n",
    "print(\"Multi Heuristic avg gap: \", multi_h_avg_gap)\n",
    "print(\"Multi MIP avg gap: \", multi_mip_avg_gap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_results(results_1, results_2, title):\n",
    "    #scatter plot with the name of the instance on the x-axis incline the x-axis labels\n",
    "    # and the gap on the y-axis\n",
    "    fig, ax = plt.subplots(figsize=(20, 10))\n",
    "    ax.bar([result[0] for result in results_1], [result[3] for result in results_1], label=\"Heuristic\")\n",
    "    ax.bar([result[0] for result in results_2], [result[3] for result in results_2], label=\"MIP\")\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(\"Instance\")\n",
    "    ax.set_yscale(\"symlog\")\n",
    "    ax.set_ylabel(\"Gap (%)\")\n",
    "    ax.legend()\n",
    "    plt.xticks(rotation=80)\n",
    "    plt.show()\n",
    "\n",
    "plot_results(single_h_results, single_mip_results, \"Results of instances of type 'single'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(multi_h_results, multi_mip_results, \"Results of instances of type 'multi'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_to_latex_array(data, title):\n",
    "    # Initialize the result string\n",
    "    result = \"\\\\begin{table}[h]\\n\"\n",
    "    result += \"\\\\begin{center}\\n\"\n",
    "    result += \"\\\\begin{tabular}{|p{5cm}|c|c|c|}\\n\"\n",
    "    result += \"\\\\hline\\n\"\n",
    "    result += \"\\\\textbf{Instance} & \\\\textbf{Runtime (sec)} & \\\\textbf{Obj. Value} & \\\\textbf{Gap (\\%)} \\\\\\\\\\n\" + \"\\\\hline\\n\"\n",
    "    # Iterate over the rows of the data\n",
    "    for row in data:\n",
    "        if row[3] <= 0.0001:\n",
    "            row[0] = \"\\\\textbf{\" + row[0] + \"}\"\n",
    "        # Iterate over the elements of the row and add them to the result string\n",
    "        for element in row:\n",
    "            # If the element is a string, enclose it in quotation marks\n",
    "            if isinstance(element, str):\n",
    "                result += element.replace(\"_\", \"\\_\") + \" & \"\n",
    "            # Otherwise, just convert it to a string and add it to the result string\n",
    "            else:\n",
    "                if element <= 0.0001:\n",
    "                    result += \"\\\\textbf{\"+ str(element) + \"} & \"\n",
    "                else:\n",
    "                    result += str(element) + \" & \"\n",
    "        # Remove the last \" & \" and add a newline character\n",
    "        result = result[:-2] + \" \\\\\\\\\\n\"\n",
    "\n",
    "    # Close the array\n",
    "    result += \"\\\\hline\\n\" + \"\\\\end{tabular}\\n\"\n",
    "    result += \"\\\\end{center}\\n\"\n",
    "    result += \"\\\\caption{\" + title + \"}\\n\"\n",
    "    result += \"\\\\label{tab:\" + title + \"}\\n\"\n",
    "    result += \"\\\\end{table}\\n\"\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[h]\n",
      "\\begin{center}\n",
      "\\begin{tabular}{|p{5cm}|c|c|c|}\n",
      "\\hline\n",
      "\\textbf{Instance} & \\textbf{Runtime (sec)} & \\textbf{Obj. Value} & \\textbf{Gap (\\%)} \\\\\n",
      "\\hline\n",
      "Single\\_I10\\_J50\\_T3\\_0 & \\textbf{0.0} & 23484 & 1.2008  \\\\\n",
      "Single\\_I10\\_J50\\_T3\\_1 & \\textbf{0.0} & 24301 & 0.6913  \\\\\n",
      "Single\\_I20\\_J1000\\_T8\\_0 & 0.18 & 548961 & 5.8013  \\\\\n",
      "Single\\_I20\\_J1000\\_T8\\_1 & 0.17 & 529254 & 3.5657  \\\\\n",
      "Single\\_I20\\_J100\\_T4\\_0 & 0.01 & 45097 & 1.3748  \\\\\n",
      "Single\\_I20\\_J100\\_T4\\_1 & \\textbf{0.0} & 44157 & 1.6419  \\\\\n",
      "Single\\_I20\\_J100\\_T8\\_0 & 0.02 & 81883 & 2.1812  \\\\\n",
      "Single\\_I20\\_J100\\_T8\\_1 & 0.02 & 80797 & 1.8924  \\\\\n",
      "Single\\_I20\\_J250\\_T4\\_0 & 0.01 & 87633 & 1.7847  \\\\\n",
      "Single\\_I20\\_J250\\_T4\\_1 & 0.01 & 90916 & 3.0259  \\\\\n",
      "Single\\_I20\\_J250\\_T8\\_0 & 0.04 & 161142 & 4.4366  \\\\\n",
      "Single\\_I20\\_J250\\_T8\\_1 & 0.04 & 156920 & 2.7931  \\\\\n",
      "Single\\_I30\\_J1000\\_T12\\_0 & 0.47 & 776119 & 5.7209  \\\\\n",
      "Single\\_I30\\_J1000\\_T12\\_1 & 0.48 & 793688 & 128.7386  \\\\\n",
      "Single\\_I30\\_J1000\\_T8\\_0 & 0.21 & 535028 & 6.9304  \\\\\n",
      "Single\\_I30\\_J1000\\_T8\\_1 & 0.22 & 525009 & 7.5711  \\\\\n",
      "Single\\_I30\\_J250\\_T12\\_0 & 0.13 & 221491 & 3.2546  \\\\\n",
      "Single\\_I30\\_J250\\_T12\\_1 & 0.12 & 229222 & 6.5018  \\\\\n",
      "Single\\_I30\\_J250\\_T4\\_0 & 0.01 & 88461 & 2.6562  \\\\\n",
      "Single\\_I30\\_J250\\_T4\\_1 & 0.01 & 86863 & 2.3942  \\\\\n",
      "Single\\_I30\\_J250\\_T8\\_0 & 0.05 & 154812 & 3.5248  \\\\\n",
      "Single\\_I30\\_J250\\_T8\\_1 & 0.05 & 158698 & 4.6273  \\\\\n",
      "Single\\_I30\\_J500\\_T12\\_0 & 0.24 & 401471 & 9.575  \\\\\n",
      "Single\\_I30\\_J500\\_T12\\_1 & 0.24 & 413236 & 9.487  \\\\\n",
      "Single\\_I30\\_J500\\_T8\\_0 & 0.11 & 287171 & 9.5118  \\\\\n",
      "Single\\_I30\\_J500\\_T8\\_1 & 0.11 & 288331 & 6.6907  \\\\\n",
      "Single\\_I50\\_J1000\\_T12\\_0 & 0.63 & 773459 & 10.4023  \\\\\n",
      "Single\\_I50\\_J1000\\_T12\\_1 & 0.62 & 767560 & 6.5409  \\\\\n",
      "Single\\_I50\\_J1000\\_T8\\_0 & 0.28 & 512626 & 4.8296  \\\\\n",
      "Single\\_I50\\_J1000\\_T8\\_1 & 0.27 & 532711 & 9.0621  \\\\\n",
      "Single\\_I50\\_J250\\_T12\\_0 & 0.16 & 229611 & 5.2701  \\\\\n",
      "Single\\_I50\\_J250\\_T12\\_1 & 0.16 & 228604 & 5.3463  \\\\\n",
      "Single\\_I50\\_J250\\_T8\\_0 & 0.07 & 157450 & 5.6469  \\\\\n",
      "Single\\_I50\\_J250\\_T8\\_1 & 0.07 & 160494 & 6.3209  \\\\\n",
      "Single\\_I50\\_J500\\_T12\\_0 & 0.31 & 405883 & 5.7797  \\\\\n",
      "Single\\_I50\\_J500\\_T12\\_1 & 0.32 & 409894 & 11.3689  \\\\\n",
      "Single\\_I50\\_J500\\_T8\\_0 & 0.15 & 282693 & 8.1112  \\\\\n",
      "Single\\_I50\\_J500\\_T8\\_1 & 0.13 & 284348 & 6.0763  \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n",
      "\\end{center}\n",
      "\\caption{Results of instances of type 'single' for the heuristic}\n",
      "\\label{tab:Results of instances of type 'single' for the heuristic}\n",
      "\\end{table}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(list_to_latex_array(single_h_results, \"Results of instances of type 'single' for the heuristic\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[h]\n",
      "\\begin{center}\n",
      "\\begin{tabular}{|p{5cm}|c|c|c|}\n",
      "\\hline\n",
      "\\textbf{Instance} & \\textbf{Runtime (sec)} & \\textbf{Obj. Value} & \\textbf{Gap (\\%)} \\\\\n",
      "\\hline\n",
      "Multi\\_I10\\_J50\\_T3\\_0 & \\textbf{0.0} & 34509 & 0.5332  \\\\\n",
      "Multi\\_I10\\_J50\\_T3\\_1 & \\textbf{0.0} & 36641 & 0.333  \\\\\n",
      "Multi\\_I20\\_J1000\\_T8\\_0 & 0.19 & 731218 & 1.976  \\\\\n",
      "Multi\\_I20\\_J1000\\_T8\\_1 & 0.2 & 807798 & 1.9723  \\\\\n",
      "Multi\\_I20\\_J100\\_T4\\_0 & 0.01 & 68701 & 1.1295  \\\\\n",
      "Multi\\_I20\\_J100\\_T4\\_1 & 0.01 & 108776 & 0.2611  \\\\\n",
      "Multi\\_I20\\_J100\\_T8\\_0 & 0.02 & 127679 & 0.9453  \\\\\n",
      "Multi\\_I20\\_J100\\_T8\\_1 & 0.02 & 116535 & 2.5307  \\\\\n",
      "Multi\\_I20\\_J250\\_T4\\_0 & 0.01 & 112459 & 1.7144  \\\\\n",
      "Multi\\_I20\\_J250\\_T4\\_1 & 0.02 & 232129 & 0.4438  \\\\\n",
      "Multi\\_I20\\_J250\\_T8\\_0 & 0.05 & 235372 & 2.6865  \\\\\n",
      "Multi\\_I20\\_J250\\_T8\\_1 & 0.05 & 209595 & 2.1174  \\\\\n",
      "Multi\\_I30\\_J1000\\_T12\\_0 & 0.54 & 1007963 & 2.7325  \\\\\n",
      "Multi\\_I30\\_J1000\\_T12\\_1 & 0.59 & 1224115 & 2.248  \\\\\n",
      "Multi\\_I30\\_J1000\\_T8\\_0 & 0.23 & 716045 & 3.0629  \\\\\n",
      "Multi\\_I30\\_J1000\\_T8\\_1 & 0.29 & 1181039 & 2.0056  \\\\\n",
      "Multi\\_I30\\_J250\\_T12\\_0 & 0.13 & 299654 & 9.8664  \\\\\n",
      "Multi\\_I30\\_J250\\_T12\\_1 & 0.14 & 276580 & 3.0103  \\\\\n",
      "Multi\\_I30\\_J250\\_T4\\_0 & 0.02 & 266514 & 0.6844  \\\\\n",
      "Multi\\_I30\\_J250\\_T4\\_1 & 0.02 & 322845 & 0.9012  \\\\\n",
      "Multi\\_I30\\_J250\\_T8\\_0 & 0.07 & 348154 & 1.1348  \\\\\n",
      "Multi\\_I30\\_J250\\_T8\\_1 & 0.07 & 360819 & 1.169  \\\\\n",
      "Multi\\_I30\\_J500\\_T12\\_0 & 0.3 & 624388 & 1.775  \\\\\n",
      "Multi\\_I30\\_J500\\_T12\\_1 & 0.29 & 625574 & 1.7002  \\\\\n",
      "Multi\\_I30\\_J500\\_T8\\_0 & 0.11 & 395191 & 2.3896  \\\\\n",
      "Multi\\_I30\\_J500\\_T8\\_1 & 0.12 & 400793 & 1.7737  \\\\\n",
      "Multi\\_I50\\_J1000\\_T12\\_0 & 0.83 & 1583818 & 1.9168  \\\\\n",
      "Multi\\_I50\\_J1000\\_T12\\_1 & 0.86 & 1555188 & 1.774  \\\\\n",
      "Multi\\_I50\\_J1000\\_T8\\_0 & 0.37 & 1206507 & 1.0359  \\\\\n",
      "Multi\\_I50\\_J1000\\_T8\\_1 & 0.42 & 1789442 & 1.0668  \\\\\n",
      "Multi\\_I50\\_J250\\_T12\\_0 & 0.22 & 497921 & 9.9937  \\\\\n",
      "Multi\\_I50\\_J250\\_T12\\_1 & 0.22 & 550365 & 1.4242  \\\\\n",
      "Multi\\_I50\\_J250\\_T8\\_0 & 0.09 & 371804 & 1.5417  \\\\\n",
      "Multi\\_I50\\_J250\\_T8\\_1 & 0.08 & 271628 & 0.8246  \\\\\n",
      "Multi\\_I50\\_J500\\_T12\\_0 & 0.34 & 529743 & 4.0592  \\\\\n",
      "Multi\\_I50\\_J500\\_T12\\_1 & 0.45 & 875421 & 1.9255  \\\\\n",
      "Multi\\_I50\\_J500\\_T8\\_0 & 0.18 & 625671 & 1.2121  \\\\\n",
      "Multi\\_I50\\_J500\\_T8\\_1 & 0.22 & 961577 & 0.845  \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n",
      "\\end{center}\n",
      "\\caption{Results of instances of type 'multi' for the heuristic}\n",
      "\\label{tab:Results of instances of type 'multi' for the heuristic}\n",
      "\\end{table}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(list_to_latex_array(multi_h_results, \"Results of instances of type 'multi' for the heuristic\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[h]\n",
      "\\begin{center}\n",
      "\\begin{tabular}{|p{5cm}|c|c|c|}\n",
      "\\hline\n",
      "\\textbf{Instance} & \\textbf{Runtime (sec)} & \\textbf{Obj. Value} & \\textbf{Gap (\\%)} \\\\\n",
      "\\hline\n",
      "\\textbf{Single\\_I10\\_J50\\_T3\\_0} & 0.14 & 23202 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Single\\_I10\\_J50\\_T3\\_1} & 0.12 & 24133 & \\textbf{0.0}  \\\\\n",
      "Single\\_I20\\_J1000\\_T8\\_0 & 290.56 & 531134 & 2.5539  \\\\\n",
      "Single\\_I20\\_J1000\\_T8\\_1 & 283.29 & 517860 & 1.4129  \\\\\n",
      "\\textbf{Single\\_I20\\_J100\\_T4\\_0} & 3.22 & 44477 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Single\\_I20\\_J100\\_T4\\_1} & 4.82 & 43432 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Single\\_I20\\_J100\\_T8\\_0} & 212.0 & 80097 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Single\\_I20\\_J100\\_T8\\_1} & 21.98 & 79268 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Single\\_I20\\_J250\\_T4\\_0} & 211.2 & 86069 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Single\\_I20\\_J250\\_T4\\_1} & 108.68 & 88165 & \\textbf{0.0}  \\\\\n",
      "Single\\_I20\\_J250\\_T8\\_0 & 300.01 & 156867 & 1.7837  \\\\\n",
      "Single\\_I20\\_J250\\_T8\\_1 & 300.2 & 153778 & 0.7908  \\\\\n",
      "Single\\_I30\\_J1000\\_T12\\_0 & 300.01 & 764049 & 4.1657  \\\\\n",
      "Single\\_I30\\_J1000\\_T12\\_1 & 300.25 & 1021783 & 100.0  \\\\\n",
      "Single\\_I30\\_J1000\\_T8\\_0 & 209.58 & 538790 & 6.2273  \\\\\n",
      "Single\\_I30\\_J1000\\_T8\\_1 & 252.8 & 534269 & 5.8073  \\\\\n",
      "Single\\_I30\\_J250\\_T12\\_0 & 300.02 & 220905 & 2.99  \\\\\n",
      "Single\\_I30\\_J250\\_T12\\_1 & 300.01 & 231221 & 5.6297  \\\\\n",
      "Single\\_I30\\_J250\\_T4\\_0 & 299.81 & 86451 & 0.384  \\\\\n",
      "Single\\_I30\\_J250\\_T4\\_1 & 300.08 & 85118 & 0.3853  \\\\\n",
      "Single\\_I30\\_J250\\_T8\\_0 & 300.01 & 151186 & 1.1826  \\\\\n",
      "Single\\_I30\\_J250\\_T8\\_1 & 300.26 & 157002 & 3.5586  \\\\\n",
      "Single\\_I30\\_J500\\_T12\\_0 & 300.03 & 412524 & 6.8219  \\\\\n",
      "Single\\_I30\\_J500\\_T12\\_1 & 164.44 & 421898 & 7.3909  \\\\\n",
      "Single\\_I30\\_J500\\_T8\\_0 & 162.3 & 293992 & 7.1366  \\\\\n",
      "Single\\_I30\\_J500\\_T8\\_1 & 300.01 & 290472 & 5.9482  \\\\\n",
      "Single\\_I50\\_J1000\\_T12\\_0 & 221.91 & 789867 & 8.2809  \\\\\n",
      "Single\\_I50\\_J1000\\_T12\\_1 & 277.72 & 769770 & 6.253  \\\\\n",
      "Single\\_I50\\_J1000\\_T8\\_0 & 292.76 & 510800 & 4.4734  \\\\\n",
      "Single\\_I50\\_J1000\\_T8\\_1 & 211.94 & 541740 & 7.3672  \\\\\n",
      "Single\\_I50\\_J250\\_T12\\_0 & 300.01 & 229406 & 5.1808  \\\\\n",
      "Single\\_I50\\_J250\\_T12\\_1 & 297.49 & 228851 & 5.2383  \\\\\n",
      "Single\\_I50\\_J250\\_T8\\_0 & 218.86 & 158055 & 5.2627  \\\\\n",
      "Single\\_I50\\_J250\\_T8\\_1 & 300.01 & 161124 & 5.9284  \\\\\n",
      "Single\\_I50\\_J500\\_T12\\_0 & 262.13 & 407385 & 5.4096  \\\\\n",
      "Single\\_I50\\_J500\\_T12\\_1 & 280.23 & 421229 & 8.6036  \\\\\n",
      "Single\\_I50\\_J500\\_T8\\_0 & 286.67 & 286869 & 6.634  \\\\\n",
      "Single\\_I50\\_J500\\_T8\\_1 & 194.12 & 278307 & 3.9518  \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n",
      "\\end{center}\n",
      "\\caption{Results of instances of type 'single' for the MIP}\n",
      "\\label{tab:Results of instances of type 'single' for the MIP}\n",
      "\\end{table}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(list_to_latex_array(single_mip_results, \"Results of instances of type 'single' for the MIP\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[h]\n",
      "\\begin{center}\n",
      "\\begin{tabular}{|p{5cm}|c|c|c|}\n",
      "\\hline\n",
      "\\textbf{Instance} & \\textbf{Runtime (sec)} & \\textbf{Obj. Value} & \\textbf{Gap (\\%)} \\\\\n",
      "\\hline\n",
      "\\textbf{Multi\\_I10\\_J50\\_T3\\_0} & 0.24 & 34325 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I10\\_J50\\_T3\\_1} & 0.14 & 36519 & \\textbf{0.0}  \\\\\n",
      "Multi\\_I20\\_J1000\\_T8\\_0 & 269.39 & 718670 & 0.26  \\\\\n",
      "Multi\\_I20\\_J1000\\_T8\\_1 & 284.8 & 797646 & 0.7156  \\\\\n",
      "\\textbf{Multi\\_I20\\_J100\\_T4\\_0} & 0.51 & 67925 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I20\\_J100\\_T4\\_1} & 0.32 & 108492 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I20\\_J100\\_T8\\_0} & 5.89 & 126472 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I20\\_J100\\_T8\\_1} & 23.27 & 113586 & \\textbf{0.0001}  \\\\\n",
      "\\textbf{Multi\\_I20\\_J250\\_T4\\_0} & 50.92 & 110531 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I20\\_J250\\_T4\\_1} & 2.44 & 231099 & \\textbf{0.0001}  \\\\\n",
      "Multi\\_I20\\_J250\\_T8\\_0 & 300.02 & 229872 & 0.3498  \\\\\n",
      "Multi\\_I20\\_J250\\_T8\\_1 & 300.03 & 205247 & 0.0429  \\\\\n",
      "Multi\\_I30\\_J1000\\_T12\\_0 & 278.87 & 1004033 & 2.3426  \\\\\n",
      "Multi\\_I30\\_J1000\\_T12\\_1 & 179.72 & 1210809 & 1.161  \\\\\n",
      "Multi\\_I30\\_J1000\\_T8\\_0 & 265.64 & 711226 & 2.3899  \\\\\n",
      "Multi\\_I30\\_J1000\\_T8\\_1 & 300.0 & 1164861 & 0.6358  \\\\\n",
      "Multi\\_I30\\_J250\\_T12\\_0 & 300.03 & 310252 & 6.3297  \\\\\n",
      "Multi\\_I30\\_J250\\_T12\\_1 & 300.02 & 276620 & 2.9958  \\\\\n",
      "\\textbf{Multi\\_I30\\_J250\\_T4\\_0} & 9.26 & 264690 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I30\\_J250\\_T4\\_1} & 7.73 & 319936 & \\textbf{0.0001}  \\\\\n",
      "\\textbf{Multi\\_I30\\_J250\\_T8\\_0} & 20.31 & 344203 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I30\\_J250\\_T8\\_1} & 9.26 & 356601 & \\textbf{0.0}  \\\\\n",
      "Multi\\_I30\\_J500\\_T12\\_0 & 300.1 & 620885 & 1.214  \\\\\n",
      "Multi\\_I30\\_J500\\_T12\\_1 & 291.39 & 620104 & 0.8258  \\\\\n",
      "Multi\\_I30\\_J500\\_T8\\_0 & 280.42 & 392735 & 1.7681  \\\\\n",
      "Multi\\_I30\\_J500\\_T8\\_1 & 300.02 & 396201 & 0.628  \\\\\n",
      "Multi\\_I50\\_J1000\\_T12\\_0 & 239.02 & 1576999 & 1.4863  \\\\\n",
      "Multi\\_I50\\_J1000\\_T12\\_1 & 294.76 & 1544956 & 1.1161  \\\\\n",
      "\\textbf{Multi\\_I50\\_J1000\\_T8\\_0} & 201.37 & 1194010 & \\textbf{0.0001}  \\\\\n",
      "Multi\\_I50\\_J1000\\_T8\\_1 & 300.0 & 1770725 & 0.0208  \\\\\n",
      "Multi\\_I50\\_J250\\_T12\\_0 & 299.07 & 519762 & 5.6073  \\\\\n",
      "\\textbf{Multi\\_I50\\_J250\\_T12\\_1} & 78.33 & 542527 & \\textbf{0.0001}  \\\\\n",
      "Multi\\_I50\\_J250\\_T8\\_0 & 290.98 & 370637 & 1.2278  \\\\\n",
      "Multi\\_I50\\_J250\\_T8\\_1 & 294.09 & 269988 & 0.2208  \\\\\n",
      "Multi\\_I50\\_J500\\_T12\\_0 & 270.13 & 530544 & 3.908  \\\\\n",
      "Multi\\_I50\\_J500\\_T12\\_1 & 300.0 & 869707 & 1.2728  \\\\\n",
      "\\textbf{Multi\\_I50\\_J500\\_T8\\_0} & 180.99 & 618087 & \\textbf{0.0}  \\\\\n",
      "\\textbf{Multi\\_I50\\_J500\\_T8\\_1} & 205.55 & 953452 & \\textbf{0.0}  \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n",
      "\\end{center}\n",
      "\\caption{Results of instances of type 'multi' for the MIP}\n",
      "\\label{tab:Results of instances of type 'multi' for the MIP}\n",
      "\\end{table}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(list_to_latex_array(multi_mip_results, \"Results of instances of type 'multi' for the MIP\"))"
   ]
  }
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