Exercício2_c.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6884894",
   "metadata": {},
   "outputs": [],
   "source": [
    "#conda install -c conda-forge glpk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fcd63c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyomo.environ as pyEnv\n",
    "from time import process_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5ce2751",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1_start = process_time() \n",
    "Q = []\n",
    "#para 1\n",
    "for i in list(range(1,9)):\n",
    "    Q.append([i])\n",
    "    \n",
    "#para 2            \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        if i != j:\n",
    "            Q.append([i,j])\n",
    "            \n",
    "#para 3          \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        for k in list(range(1,9)):\n",
    "            if i != j and j != k and k != i  :\n",
    "                Q.append([i,j,k])     \n",
    "                \n",
    "#para 4          \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        for k in list(range(1,9)):\n",
    "            for l in list(range(1,9)):\n",
    "                if i != j and j != k and k != i and l !=i and l !=j and l !=k :\n",
    "                    Q.append([i,j,k,l]) \n",
    "#para 5          \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        for k in list(range(1,9)):\n",
    "            for l in list(range(1,9)):\n",
    "                for m in list(range(1,9)):\n",
    "                    if i != j and j != k and k != i and l !=i and l !=j and l !=k and m!=i and m != j and m!=k and m!=l:\n",
    "                        Q.append([i,j,k,l,m])    \n",
    "#para 5          \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        for k in list(range(1,9)):\n",
    "            for l in list(range(1,9)):\n",
    "                for m in list(range(1,9)):\n",
    "                    if i != j and j != k and k != i and l !=i and l !=j and l !=k and m!=i and m != j and m!=k and m!=l:\n",
    "                        Q.append([i,j,k,l,m])                          \n",
    "#para 6          \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        for k in list(range(1,9)):\n",
    "            for l in list(range(1,9)):\n",
    "                for m in list(range(1,9)):\n",
    "                    for n in list(range(1,9)):\n",
    "                        if i != j and j != k and k != i and l !=i and l !=j and l !=k and m!=i and m != j and m!=k and m!=l and n!=i and n!=j and n!= k and n!=l and n!=m:\n",
    "                            Q.append([i,j,k,l,m,n])    \n",
    "#para 7          \n",
    "for i in list(range(1,9)):\n",
    "    for j in list(range(1,9)):\n",
    "        for k in list(range(1,9)):\n",
    "            for l in list(range(1,9)):\n",
    "                for m in list(range(1,9)):\n",
    "                    for n in list(range(1,9)):\n",
    "                        for o in list(range(1,9)):\n",
    "                            if i != j and j != k and k != i and l !=i and l !=j and l !=k and m!=i and m != j and m!=k and m!=l and n!=i and n!=j and n!= k and n!=l and n!=m and o!=i and o!=j and o!=k and o != l and o!=m and o!=n:\n",
    "                                Q.append([i,j,k,l,m,n,o])\n",
    "\n",
    "NQ = []\n",
    "for q in Q:\n",
    "    v=[]\n",
    "    for i in list(range(1,9)):\n",
    "        if not i in q:\n",
    "            v.insert(0,i)\n",
    "    NQ.append(v) \n",
    "\n",
    "#print(NQ)    \n",
    "\n",
    "with open(\"file.txt\", \"w\") as f:\n",
    "    for q in range(len(Q)):\n",
    "        f.write(str(Q[q]) + str(NQ[q]) + \"\\n\")\n",
    "c_ij=[\n",
    "    [0,6,8,5,999999,999999,999999,999999],\n",
    "    [6,0,9,999999,999999,999999,8,999999],\n",
    "    [8,9,0,7,999999,10,20,999999],\n",
    "    [5,999999,7,0,22,999999,999999,999999],\n",
    "    [999999,999999,999999,22,0,999999,999999,9],\n",
    "    [999999,999999,10,999999,999999,0,7,4],\n",
    "    [999999,8,20,999999,999999,7,0,7],\n",
    "    [999999,999999,999999,999999,9,4,7,0]\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "157aba02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#indices\n",
    "modelo = pyEnv.ConcreteModel()\n",
    "\n",
    "modelo.I = pyEnv.RangeSet(len([1,2,3,4,5,6,7,8]))\n",
    "modelo.J = pyEnv.RangeSet(len([1,2,3,4,5,6,7,8]))\n",
    "modelo.Q = pyEnv.RangeSet(len(Q))\n",
    "modelo.NQ = pyEnv.RangeSet(len(NQ))\n",
    "\n",
    "#Variáveis\n",
    "modelo.x_ij = pyEnv.Var(modelo.I,modelo.J,within = pyEnv.Binary)\n",
    "\n",
    "#parâmetros\n",
    "modelo.C_ij = pyEnv.Param(modelo.I,modelo.J,initialize = lambda modelo,i,j:c_ij[i-1][j -1])\n",
    "\n",
    "#Função objetivo\n",
    "def func_objetivo(modelo):\n",
    "    return sum(modelo.x_ij[i,j] * modelo.C_ij[i,j] for i in modelo.I for j in modelo.J)\n",
    "\n",
    "modelo.objetivo = pyEnv.Objective(rule = func_objetivo ,sense = pyEnv.minimize) \n",
    "\n",
    "\n",
    "def SomaI(modelo,j): \n",
    "    return   1  == sum(modelo.x_ij[i,j] for i in modelo.I if i != j )\n",
    "\n",
    "modelo.somaI = pyEnv.Constraint(modelo.J, rule = SomaI)    \n",
    "\n",
    "def SomaJ(modelo,i): \n",
    "    return   1  == sum(modelo.x_ij[i,j] for j in modelo.J if i != j )    \n",
    "\n",
    "modelo.somaJ = pyEnv.Constraint(modelo.I, rule = SomaJ) \n",
    "\n",
    "def SubRota(modelo,q):\n",
    "    expr = 0\n",
    "    for i in Q[q-1]:\n",
    "        for j in NQ[q-1]:\n",
    "            expr +=modelo.x_ij[i,j]\n",
    "    #print(expr,Q[q-1],NQ[q-1])        \n",
    "    return expr >= 1            \n",
    "\n",
    "modelo.subRota = pyEnv.Constraint(modelo.Q, rule = SubRota)    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d8c8b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLPSOL--GLPK LP/MIP Solver 5.0\n",
      "Parameter(s) specified in the command line:\n",
      " --write C:\\Users\\User\\AppData\\Local\\Temp\\tmpo5ibpfvg.glpk.raw --wglp C:\\Users\\User\\AppData\\Local\\Temp\\tmp7qq4i53u.glpk.glp\n",
      " --cpxlp C:\\Users\\User\\AppData\\Local\\Temp\\tmpdi1dvls5.pyomo.lp\n",
      "Reading problem data from 'C:\\Users\\User\\AppData\\Local\\Temp\\tmpdi1dvls5.pyomo.lp'...\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\tmpdi1dvls5.pyomo.lp:986693: warning: lower bound of variable 'x2' redefined\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\tmpdi1dvls5.pyomo.lp:986693: warning: upper bound of variable 'x2' redefined\n",
      "76017 rows, 57 columns, 758521 non-zeros\n",
      "56 integer variables, all of which are binary\n",
      "986749 lines were read\n",
      "Writing problem data to 'C:\\Users\\User\\AppData\\Local\\Temp\\tmp7qq4i53u.glpk.glp'...\n",
      "910672 lines were written\n",
      "GLPK Integer Optimizer 5.0\n",
      "76017 rows, 57 columns, 758521 non-zeros\n",
      "56 integer variables, all of which are binary\n",
      "Preprocessing...\n",
      "76016 rows, 56 columns, 758520 non-zeros\n",
      "56 integer variables, all of which are binary\n",
      "Scaling...\n",
      " A: min|aij| =  1.000e+00  max|aij| =  1.000e+00  ratio =  1.000e+00\n",
      "Problem data seem to be well scaled\n",
      "Constructing initial basis...\n",
      "Size of triangular part is 76015\n",
      "Solving LP relaxation...\n",
      "GLPK Simplex Optimizer 5.0\n",
      "76016 rows, 56 columns, 758520 non-zeros\n",
      "      0: obj =   6.000005000e+06 inf =   1.481e+04 (7783)\n",
      "      8: obj =   4.000038000e+06 inf =   0.000e+00 (0)\n",
      "*    28: obj =   7.100000000e+01 inf =   0.000e+00 (0)\n",
      "OPTIMAL LP SOLUTION FOUND\n",
      "Integer optimization begins...\n",
      "Long-step dual simplex will be used\n",
      "+    28: mip =     not found yet >=              -inf        (1; 0)\n",
      "+    28: >>>>>   7.100000000e+01 >=   7.100000000e+01   0.0% (1; 0)\n",
      "+    28: mip =   7.100000000e+01 >=     tree is empty   0.0% (0; 1)\n",
      "INTEGER OPTIMAL SOLUTION FOUND\n",
      "Time used:   0.7 secs\n",
      "Memory used: 164.8 Mb (172788000 bytes)\n",
      "Writing MIP solution to 'C:\\Users\\User\\AppData\\Local\\Temp\\tmpo5ibpfvg.glpk.raw'...\n",
      "76083 lines were written\n",
      "Tempo para execução do programa em segundos: 6.828125\n"
     ]
    }
   ],
   "source": [
    "solver = pyEnv.SolverFactory('glpk')\n",
    "resultado_objetivo = solver.solve(modelo,tee = True)\n",
    "t1_stop = process_time()\n",
    "print(\"Tempo para execução do programa em segundos:\", t1_stop-t1_start) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c7745f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Problem: \n",
      "- Name: unknown\n",
      "  Lower bound: 71.0\n",
      "  Upper bound: 71.0\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 76017\n",
      "  Number of variables: 57\n",
      "  Number of nonzeros: 758521\n",
      "  Sense: minimize\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 1\n",
      "      Number of created subproblems: 1\n",
      "  Error rc: 0\n",
      "  Time: 2.1089935302734375\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(resultado_objetivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9fb98ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I. 1 J. 1 -----> None\n",
      "I. 1 J. 2 -----> 0.0\n",
      "I. 1 J. 3 -----> 1.0\n",
      "I. 1 J. 4 -----> 0.0\n",
      "I. 1 J. 5 -----> 0.0\n",
      "I. 1 J. 6 -----> 0.0\n",
      "I. 1 J. 7 -----> 0.0\n",
      "I. 1 J. 8 -----> 0.0\n",
      "I. 2 J. 1 -----> 1.0\n",
      "I. 2 J. 2 -----> None\n",
      "I. 2 J. 3 -----> 0.0\n",
      "I. 2 J. 4 -----> 0.0\n",
      "I. 2 J. 5 -----> 0.0\n",
      "I. 2 J. 6 -----> 0.0\n",
      "I. 2 J. 7 -----> 0.0\n",
      "I. 2 J. 8 -----> 0.0\n",
      "I. 3 J. 1 -----> 0.0\n",
      "I. 3 J. 2 -----> 0.0\n",
      "I. 3 J. 3 -----> None\n",
      "I. 3 J. 4 -----> 1.0\n",
      "I. 3 J. 5 -----> 0.0\n",
      "I. 3 J. 6 -----> 0.0\n",
      "I. 3 J. 7 -----> 0.0\n",
      "I. 3 J. 8 -----> 0.0\n",
      "I. 4 J. 1 -----> 0.0\n",
      "I. 4 J. 2 -----> 0.0\n",
      "I. 4 J. 3 -----> 0.0\n",
      "I. 4 J. 4 -----> None\n",
      "I. 4 J. 5 -----> 1.0\n",
      "I. 4 J. 6 -----> 0.0\n",
      "I. 4 J. 7 -----> 0.0\n",
      "I. 4 J. 8 -----> 0.0\n",
      "I. 5 J. 1 -----> 0.0\n",
      "I. 5 J. 2 -----> 0.0\n",
      "I. 5 J. 3 -----> 0.0\n",
      "I. 5 J. 4 -----> 0.0\n",
      "I. 5 J. 5 -----> None\n",
      "I. 5 J. 6 -----> 0.0\n",
      "I. 5 J. 7 -----> 0.0\n",
      "I. 5 J. 8 -----> 1.0\n",
      "I. 6 J. 1 -----> 0.0\n",
      "I. 6 J. 2 -----> 0.0\n",
      "I. 6 J. 3 -----> 0.0\n",
      "I. 6 J. 4 -----> 0.0\n",
      "I. 6 J. 5 -----> 0.0\n",
      "I. 6 J. 6 -----> None\n",
      "I. 6 J. 7 -----> 1.0\n",
      "I. 6 J. 8 -----> 0.0\n",
      "I. 7 J. 1 -----> 0.0\n",
      "I. 7 J. 2 -----> 1.0\n",
      "I. 7 J. 3 -----> 0.0\n",
      "I. 7 J. 4 -----> 0.0\n",
      "I. 7 J. 5 -----> 0.0\n",
      "I. 7 J. 6 -----> 0.0\n",
      "I. 7 J. 7 -----> None\n",
      "I. 7 J. 8 -----> 0.0\n",
      "I. 8 J. 1 -----> 0.0\n",
      "I. 8 J. 2 -----> 0.0\n",
      "I. 8 J. 3 -----> 0.0\n",
      "I. 8 J. 4 -----> 0.0\n",
      "I. 8 J. 5 -----> 0.0\n",
      "I. 8 J. 6 -----> 1.0\n",
      "I. 8 J. 7 -----> 0.0\n",
      "I. 8 J. 8 -----> None\n"
     ]
    }
   ],
   "source": [
    "for i in modelo.I:\n",
    "    for j in modelo.J:\n",
    "        print(\"I.\", i, \"J.\", j, \"----->\", modelo.x_ij[i,j]())\n"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "4cd67947",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d927c92b",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}