metaheuristic_multiobjetive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "# importing required modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compact(placement_cpu, placement_job,num_job,num_mc):\n",
    "    while True:\n",
    "        move_count = 0\n",
    "        for job_id,job in enumerate(placement_cpu):\n",
    "            for i,task in enumerate(job):\n",
    "                if i != num_job-1:\n",
    "\n",
    "                    #CPU gap\n",
    "                    curr_task_end = task[2] + task[3]\n",
    "                    next_cpu_task_ini = job[i+1][2]\n",
    "                    gap_cpu = next_cpu_task_ini - curr_task_end\n",
    "\n",
    "                    #JOB gap\n",
    "                    job_n = task[0]\n",
    "                    task_n = task[1]\n",
    "                    gap_job = 0\n",
    "                    if task_n + 1 < num_job:\n",
    "                        next_job_task_ini = placement_job[job_n][task_n+1][2]\n",
    "                        gap_job = next_job_task_ini - curr_task_end\n",
    "                    elif task_n + 1 == num_job:\n",
    "                        gap_job = gap_cpu\n",
    "                    #gap\n",
    "                    gap = min(gap_cpu, gap_job)\n",
    "                    #print(gap)\n",
    "\n",
    "                    #Update init position: new_start = old_start + gap\n",
    "                    if gap:\n",
    "                        #Update placement_cpu array\n",
    "                        task[2] = task[2] + gap  \n",
    "                        #Update placement_job array\n",
    "                        placement_job[job_n][task_n][2] = task[2]\n",
    "\n",
    "                        move_count = move_count + 1\n",
    "\n",
    "        if move_count == 0:\n",
    "            break\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gap(placement_cpu):\n",
    "    gap = 0\n",
    "    for job_id,job in enumerate(placement_cpu):\n",
    "            start_point = job[0][2]\n",
    "            stop_point = job[-1][2]+job[-1][3]\n",
    "            span = stop_point - start_point\n",
    "            duration = 0\n",
    "            for i,task in enumerate(job):\n",
    "                duration += task[3]\n",
    "            gap += (span - duration)\n",
    "    return gap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def metaheuristic_multiobjetive(num_job, num_mc, ms, pt, plot=False):\n",
    "\n",
    "\n",
    "    num_gene=num_mc*num_job # number of genes in a chromosome\n",
    "    \n",
    "    population_size= 300 # default value is 30\n",
    "    crossover_rate= 0.8 # default value is 0.8\n",
    "    mutation_rate= 0.3 # default value is 0.2\n",
    "    mutation_selection_rate=0.2\n",
    "    num_iteration = 500 #2000\n",
    "    \n",
    "    num_mutation_jobs=round(num_gene*mutation_selection_rate)\n",
    "    \n",
    "\n",
    "    start_time = time.time()\n",
    "\n",
    "    '''==================== main code ==============================='''\n",
    "    '''----- generate initial population -----'''\n",
    "    Tbest=999999999999999\n",
    "    best_list,best_obj=[],[]\n",
    "    population_list=[]\n",
    "    makespan_record=[]\n",
    "    for i in range(population_size):\n",
    "        nxm_random_num=list(np.random.permutation(num_gene)) # generate a random permutation of 0 to num_job*num_mc-1\n",
    "        population_list.append(nxm_random_num) # add to the population_list\n",
    "        for j in range(num_gene):\n",
    "            population_list[i][j]=population_list[i][j]%num_job # convert to job number format, every job appears m times\n",
    "\n",
    "    for n in range(num_iteration):\n",
    "        print(\".\",end = '')\n",
    "        Tbest_now=99999999999           \n",
    "\n",
    "        '''-------- two point crossover --------'''\n",
    "        parent_list=copy.deepcopy(population_list)\n",
    "        offspring_list=copy.deepcopy(population_list)\n",
    "        S=list(np.random.permutation(population_size)) # generate a random sequence to select the parent chromosome to crossover\n",
    "\n",
    "        for m in range(int(population_size/2)):\n",
    "            crossover_prob=np.random.rand()\n",
    "            if crossover_rate>=crossover_prob:\n",
    "                parent_1= population_list[S[2*m]][:]\n",
    "                parent_2= population_list[S[2*m+1]][:]\n",
    "                child_1=parent_1[:]\n",
    "                child_2=parent_2[:]\n",
    "                cutpoint=list(np.random.choice(num_gene, 2, replace=False))\n",
    "                cutpoint.sort()\n",
    "\n",
    "                child_1[cutpoint[0]:cutpoint[1]]=parent_2[cutpoint[0]:cutpoint[1]]\n",
    "                child_2[cutpoint[0]:cutpoint[1]]=parent_1[cutpoint[0]:cutpoint[1]]\n",
    "                offspring_list[S[2*m]]=child_1[:]\n",
    "                offspring_list[S[2*m+1]]=child_2[:]\n",
    "\n",
    "\n",
    "        '''----------repairment-------------'''\n",
    "        for m in range(population_size):\n",
    "            job_count={}\n",
    "            larger,less=[],[] # 'larger' record jobs appear in the chromosome more than m times, and 'less' records less than m times.\n",
    "            for i in range(num_job):\n",
    "                if i in offspring_list[m]:\n",
    "                    count=offspring_list[m].count(i)\n",
    "                    pos=offspring_list[m].index(i)\n",
    "                    job_count[i]=[count,pos] # store the above two values to the job_count dictionary\n",
    "                else:\n",
    "                    count=0\n",
    "                    job_count[i]=[count,0]\n",
    "                if count>num_mc:\n",
    "                    larger.append(i)\n",
    "                elif count<num_mc:\n",
    "                    less.append(i)\n",
    "\n",
    "            for k in range(len(larger)):\n",
    "                chg_job=larger[k]\n",
    "                while job_count[chg_job][0]>num_mc:\n",
    "                    for d in range(len(less)):\n",
    "                        if job_count[less[d]][0]<num_mc:                    \n",
    "                            offspring_list[m][job_count[chg_job][1]]=less[d]\n",
    "                            job_count[chg_job][1]=offspring_list[m].index(chg_job)\n",
    "                            job_count[chg_job][0]=job_count[chg_job][0]-1\n",
    "                            job_count[less[d]][0]=job_count[less[d]][0]+1                    \n",
    "                        if job_count[chg_job][0]==num_mc:\n",
    "                            break     \n",
    "\n",
    "        '''--------mutation--------'''   \n",
    "        for m in range(len(offspring_list)):\n",
    "            mutation_prob=np.random.rand()\n",
    "            if mutation_rate >= mutation_prob:\n",
    "                m_chg=list(np.random.choice(num_gene, num_mutation_jobs, replace=False)) # chooses the position to mutation\n",
    "                t_value_last=offspring_list[m][m_chg[0]] # save the value which is on the first mutation position\n",
    "                for i in range(num_mutation_jobs-1):\n",
    "                    offspring_list[m][m_chg[i]]=offspring_list[m][m_chg[i+1]] # displacement\n",
    "\n",
    "                offspring_list[m][m_chg[num_mutation_jobs-1]]=t_value_last # move the value of the first mutation position to the last mutation position\n",
    "\n",
    "\n",
    "        '''--------fitness value(calculate makespan)-------------'''\n",
    "        total_chromosome=copy.deepcopy(parent_list)+copy.deepcopy(offspring_list) # parent and offspring chromosomes combination\n",
    "        chrom_fitness,chrom_fit=[],[]\n",
    "        total_fitness=0\n",
    "        for m in range(population_size*2):\n",
    "            j_keys=[j for j in range(num_job)]\n",
    "            key_count={key:0 for key in j_keys}\n",
    "            j_count={key:0 for key in j_keys}\n",
    "            m_keys=[j+1 for j in range(num_mc)]\n",
    "            m_count={key:0 for key in m_keys}\n",
    "            placement_cpu = [[] for m in range(num_mc)] #NEW\n",
    "            placement_job = [[] for m in range(num_job)] #NEW\n",
    "            \n",
    "            for i in total_chromosome[m]:\n",
    "                gen_t=int(pt[i][key_count[i]])\n",
    "                gen_m=int(ms[i][key_count[i]])\n",
    "                j_count[i]=j_count[i]+gen_t\n",
    "                m_count[gen_m]=m_count[gen_m]+gen_t\n",
    "\n",
    "                if m_count[gen_m]<j_count[i]:\n",
    "                    m_count[gen_m]=j_count[i]\n",
    "                elif m_count[gen_m]>j_count[i]:\n",
    "                    j_count[i]=m_count[gen_m]\n",
    "                \n",
    "                #array por cpu y task con [job,task,start,dur]\n",
    "                placement_cpu[gen_m-1].append([i, key_count[i], j_count[i]-gen_t, gen_t]) \n",
    "                #array por job y task [cpu,task,start,dur]\n",
    "                placement_job[i].append([gen_m-1, key_count[i], j_count[i]-gen_t, gen_t])\n",
    "                \n",
    "                key_count[i]=key_count[i]+1 \n",
    "                \n",
    "            # Compact jobs (ale)\n",
    "            #print(placement_cpu)\n",
    "            compact(placement_cpu, placement_job,num_job,num_mc)\n",
    "            #print(placement_cpu)\n",
    "            \n",
    "            # Compute gaps (ale)\n",
    "            gap = compute_gap(placement_cpu)\n",
    "                        \n",
    "            makespan=max(j_count.values()) + gap\n",
    "            chrom_fitness.append(1/makespan)\n",
    "            chrom_fit.append(makespan)\n",
    "            total_fitness=total_fitness+chrom_fitness[m]\n",
    "\n",
    "        '''----------selection(roulette wheel approach)----------'''\n",
    "        pk,qk=[],[]\n",
    "\n",
    "        for i in range(population_size*2):\n",
    "            pk.append(chrom_fitness[i]/total_fitness)\n",
    "        for i in range(population_size*2):\n",
    "            cumulative=0\n",
    "            for j in range(0,i+1):\n",
    "                cumulative=cumulative+pk[j]\n",
    "            qk.append(cumulative)\n",
    "\n",
    "        selection_rand=[np.random.rand() for i in range(population_size)]\n",
    "\n",
    "        for i in range(population_size):\n",
    "            if selection_rand[i]<=qk[0]:\n",
    "                population_list[i]=copy.deepcopy(total_chromosome[0])\n",
    "            else:\n",
    "                for j in range(0,population_size*2-1):\n",
    "                    if selection_rand[i]>qk[j] and selection_rand[i]<=qk[j+1]:\n",
    "                        population_list[i]=copy.deepcopy(total_chromosome[j+1])\n",
    "                        break\n",
    "                        \n",
    "        '''----------comparison----------'''\n",
    "        for i in range(population_size*2):\n",
    "            if chrom_fit[i]<Tbest_now:\n",
    "                Tbest_now=chrom_fit[i]\n",
    "                sequence_now=copy.deepcopy(total_chromosome[i])\n",
    "        if Tbest_now<=Tbest:\n",
    "            Tbest=Tbest_now\n",
    "            sequence_best=copy.deepcopy(sequence_now)\n",
    "\n",
    "        makespan_record.append(Tbest)\n",
    "        \n",
    "        \n",
    "    '''----------result----------'''\n",
    "    print(\"optimal sequence\",sequence_best)\n",
    "    print(\"optimal value:%f\"%Tbest)\n",
    "    print('the elapsed time:%s'% (time.time() - start_time))\n",
    "    \n",
    "    \n",
    "    # Compute placements\n",
    "    \n",
    "    m_keys=[j+1 for j in range(num_mc)]\n",
    "    j_keys=[j for j in range(num_job)]\n",
    "    key_count={key:0 for key in j_keys}\n",
    "    j_count={key:0 for key in j_keys}\n",
    "    m_count={key:0 for key in m_keys}\n",
    "\n",
    "\n",
    "    placement_cpu = [[] for m in range(num_mc)]\n",
    "    placement_job = [[] for m in range(num_job)] #NEW\n",
    "\n",
    "    for i in sequence_best:\n",
    "        gen_t=int(pt[i][key_count[i]])\n",
    "        gen_m=int(ms[i][key_count[i]])\n",
    "        j_count[i]=j_count[i]+gen_t\n",
    "        m_count[gen_m]=m_count[gen_m]+gen_t\n",
    "\n",
    "        if m_count[gen_m]<j_count[i]:\n",
    "            m_count[gen_m]=j_count[i]\n",
    "        elif m_count[gen_m]>j_count[i]:\n",
    "            j_count[i]=m_count[gen_m]\n",
    "\n",
    "        #array por cpu y task con [job,task,start,dur]\n",
    "        placement_cpu[gen_m-1].append([i, key_count[i], j_count[i]-gen_t, gen_t]) \n",
    "        #array por job y task [cpu,task,start,dur]\n",
    "        placement_job[i].append([gen_m-1, key_count[i], j_count[i]-gen_t, gen_t])\n",
    "\n",
    "        key_count[i]=key_count[i]+1\n",
    "    \n",
    "    compact(placement_cpu, placement_job,num_job,num_mc)\n",
    "\n",
    "    if plot:\n",
    "        \n",
    "        import matplotlib.pyplot as plt\n",
    "        %matplotlib inline\n",
    "        plt.plot([i for i in range(len(makespan_record))],makespan_record,'b')\n",
    "        plt.ylabel('makespan',fontsize=15)\n",
    "        plt.xlabel('generation',fontsize=15)\n",
    "        plt.show()\n",
    "\n",
    "    return Tbest, placement_cpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..........................."
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-eddcf16ec96f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mTbest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplacements\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetaheuristic_multiobjetive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNB_JOBS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNB_MACHINES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-43dc0dd4d8d6>\u001b[0m in \u001b[0;36mmetaheuristic_multiobjetive\u001b[0;34m(num_job, num_mc, ms, pt, plot)\u001b[0m\n\u001b[1;32m    156\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpopulation_size\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mselection_rand\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0mqk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mselection_rand\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m<=\u001b[0m\u001b[0mqk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m                         \u001b[0mpopulation_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_chromosome\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    159\u001b[0m                         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ICML2020/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ICML2020/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ICML2020/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_nil\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_nil\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    \n",
    "    \n",
    "    # Read problem instance\n",
    "    filename = \"datasets/inference/dataset_xsmall.data\"\n",
    "\n",
    "    with open(filename, \"r\") as file:\n",
    "        NB_JOBS, NB_MACHINES = [int(v) for v in file.readline().split()]\n",
    "        JOBS = [[int(v) for v in file.readline().split()] for i in range(NB_JOBS)]\n",
    "\n",
    "    #-----------------------------------------------------------------------------\n",
    "    # Prepare the data for modeling\n",
    "    #-----------------------------------------------------------------------------\n",
    "\n",
    "    # Build list of machines. MACHINES[j][s] = id of the machine for the operation s of the job j\n",
    "    ms = [[JOBS[j][2 * s]+1 for s in range(NB_MACHINES)] for j in range(NB_JOBS)]\n",
    "\n",
    "\n",
    "    # Build list of durations. DURATION[j][s] = duration of the operation s of the job j\n",
    "    pt = [[JOBS[j][2 * s + 1] for s in range(NB_MACHINES)] for j in range(NB_JOBS)]\n",
    "\n",
    "\n",
    "    Tbest,placements = metaheuristic_multiobjetive(NB_JOBS, NB_MACHINES, ms, pt, plot=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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