TSP Gavish-Graves (GG) formulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec840b0d",
   "metadata": {},
   "source": [
    "## TSP Gavish-Graves (GG) Formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47514cb",
   "metadata": {},
   "source": [
    "##### Keywords:  TSP, GG, Gavish-Graves, Subtours, Linear Programming, Cplex, Python, Networkx, Docplex, Flow Constraint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61bc44ba",
   "metadata": {},
   "source": [
    "$ Variables $\n",
    "\n",
    "$x_{ij}={\\begin{cases}1&{\\text{the path goes from city }}i{\\text{ to city }}j\\\\0&{\\text{otherwise}}\\end{cases}}$\n",
    "\n",
    "$g_{ij}= \\text{The number of arcs on the path from vertex 1 to arc (i, j) in the optimal tour} $ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62ccbd0",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "\\begin{aligned}\n",
    "& \\underset{}{\\text{Minimize}}\n",
    "& & \\sum_{i=1}^{n} \\sum_{j=1, j\\neq i}^{n} c_{ij} x_{ij} \\\\\n",
    "& \\text{Subject to}\n",
    "& & \\sum_{j=1, j\\neq i}^{n} x_{ij} = 1, \\quad i = 1,\\ldots,n, \\\\\n",
    "& \n",
    "& & \\sum_{i=1, i\\neq j}^{n} x_{ij} = 1, \\quad j = 1,\\ldots,n, \\\\\n",
    "& \n",
    "& & \\sum_{j=1}^n g_{ij} - \\sum_{j=2}^n g_{ji} = 1, \\quad j = 2,\\ldots,n,\\\\\n",
    "&\n",
    "& & 0 \\leq g_{ij} \\leq ( n - 1 ) x_{ij}, \\quad j = 2,\\ldots,n,\\\\\n",
    "&\n",
    "& & x_{ij} \\in \\{0,1\\}, \\quad i,j = 1,\\ldots,n, \\, i\\neq j, \\\\\n",
    "&\n",
    "& & g_{ij} \\ge 0, \\quad i, j = 1,\\ldots,n,\\, i\\neq j. \\\\\n",
    "\\end{aligned}\n",
    "\\end{equation*}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a9aaf1",
   "metadata": {},
   "source": [
    "#### Import Library and Model Enviroment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be618bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docplex.mp.model import Model\n",
    "import networkx as nx   \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "mdl=Model('GG')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58fc1eca",
   "metadata": {},
   "source": [
    "#### Function to Visualize the Traveling Salesman Problem (TSP) Graphically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f526ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphic(x, d, N):\n",
    "    \"\"\"\n",
    "    Function to visualize the Traveling Salesman Problem (TSP) graphically.\n",
    "\n",
    "    Parameters:\n",
    "        x (numpy.ndarray): Distance matrix representing the pairwise distances between cities.\n",
    "        d (list): List of cities with their corresponding distances.\n",
    "        N (range): Range object representing the number of cities.\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "\n",
    "    edges = []\n",
    "    for i in N:\n",
    "        for j in N:\n",
    "            if i != j and x[i, j] > 0.5:  # This is a tolerance value, it can be adjusted as needed\n",
    "                edges.append((i, j, d[i - 1][j - 1]))\n",
    "\n",
    "    G = nx.Graph()\n",
    "    for edge in edges:\n",
    "        G.add_edge(edge[0], edge[1], weight=edge[2])\n",
    "\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    pos = nx.spring_layout(G)\n",
    "    nx.draw(G, pos, with_labels=True)\n",
    "    labels = nx.get_edge_attributes(G, 'weight')\n",
    "    nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d61d4f7",
   "metadata": {},
   "source": [
    "#### Create the Matrix of Distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6db83f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.random.randint(1, 100, size=(50, 50))\n",
    "# A_i,i = 0\n",
    "np.fill_diagonal(C, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2391ab",
   "metadata": {},
   "source": [
    "#### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c91a8114",
   "metadata": {},
   "outputs": [],
   "source": [
    "n=len(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e86774",
   "metadata": {},
   "source": [
    "#### Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d76d9b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "N=[i+1 for i in range(n)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e016faec",
   "metadata": {},
   "source": [
    "#### Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0502fcb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ind={(i,j) for i in N for j in N if i!=j}#x\n",
    "Ind2={(i,j) for i in N for j in N if i!=j}#g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d6e85d",
   "metadata": {},
   "source": [
    "#### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36faf360",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=mdl.binary_var_dict(Ind, name='x')\n",
    "g=mdl.continuous_var_dict(Ind2, name='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f460a50",
   "metadata": {},
   "source": [
    "#### Mathematical Model of Traveling Salesman Problem GG (TSP GG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efe5dbf",
   "metadata": {},
   "source": [
    "#### Objective Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c08114b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl.minimize(mdl.sum(x[i,j]*C[i-1][j-1] for i in N for j in N if i!=j))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0710248",
   "metadata": {},
   "source": [
    "#### Subject to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae1417c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in N:\n",
    "    mdl.add_constraint(mdl.sum(x[i,j] for j in N if i!=j)==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9cd0a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in N:\n",
    "    mdl.add_constraint(mdl.sum(x[i,j] for i in N if i!=j)==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "deed687d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2,n+1):\n",
    "    mdl.add_constraint(mdl.sum(g[i,j] for j in N if i!=j)-mdl.sum(g[j,i] for j in range(2,n+1) if i!=j)==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46bdc088",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in N:\n",
    "    for j in N:\n",
    "        if i!=j:\n",
    "            mdl.add_constraint(g[i,j]<=(n-1)*x[i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb989c70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version identifier: 12.10.0.0 | 2019-11-26 | 843d4de2ae\n",
      "CPXPARAM_Read_DataCheck                          1\n",
      "CPXPARAM_RandomSeed                              201903125\n",
      "Tried aggregator 1 time.\n",
      "MIP Presolve eliminated 49 rows and 49 columns.\n",
      "Reduced MIP has 2550 rows, 4851 columns, and 14455 nonzeros.\n",
      "Reduced MIP has 2450 binaries, 0 generals, 0 SOSs, and 0 indicators.\n",
      "Presolve time = 0.02 sec. (7.41 ticks)\n",
      "Found incumbent of value 2716.000000 after 0.03 sec. (21.92 ticks)\n",
      "Probing time = 0.02 sec. (4.89 ticks)\n",
      "Tried aggregator 1 time.\n",
      "Detecting symmetries...\n",
      "Reduced MIP has 2550 rows, 4851 columns, and 14455 nonzeros.\n",
      "Reduced MIP has 2450 binaries, 0 generals, 0 SOSs, and 0 indicators.\n",
      "Presolve time = 0.02 sec. (9.40 ticks)\n",
      "Probing time = 0.02 sec. (4.41 ticks)\n",
      "Clique table members: 100.\n",
      "MIP emphasis: balance optimality and feasibility.\n",
      "MIP search method: dynamic search.\n",
      "Parallel mode: deterministic, using up to 8 threads.\n",
      "Root relaxation solution time = 0.08 sec. (118.14 ticks)\n",
      "\n",
      "        Nodes                                         Cuts/\n",
      "   Node  Left     Objective  IInf  Best Integer    Best Bound    ItCnt     Gap\n",
      "\n",
      "*     0+    0                         2716.0000        0.0000           100.00%\n",
      "      0     0      189.2857    23     2716.0000      189.2857     1294   93.03%\n",
      "*     0+    0                          601.0000      189.2857            68.50%\n",
      "      0     0      194.2500    23      601.0000      Cuts: 20     2039   67.68%\n",
      "*     0+    0                          205.0000      194.2500             5.24%\n",
      "      0     0      195.0000    23      205.0000      Cuts: 61     2244    4.88%\n",
      "Detecting symmetries...\n",
      "\n",
      "Repeating presolve.\n",
      "Tried aggregator 2 times.\n",
      "MIP Presolve eliminated 2094 rows and 4230 columns.\n",
      "Aggregator did 3 substitutions.\n",
      "Reduced MIP has 453 rows, 618 columns, and 1848 nonzeros.\n",
      "Reduced MIP has 311 binaries, 0 generals, 0 SOSs, and 0 indicators.\n",
      "Presolve time = 0.02 sec. (3.39 ticks)\n",
      "Probing fixed 0 vars, tightened 6 bounds.\n",
      "Probing time = 0.00 sec. (1.07 ticks)\n",
      "Cover probing fixed 0 vars, tightened 298 bounds.\n",
      "Tried aggregator 1 time.\n",
      "Detecting symmetries...\n",
      "MIP Presolve modified 306 coefficients.\n",
      "Reduced MIP has 453 rows, 618 columns, and 1848 nonzeros.\n",
      "Reduced MIP has 311 binaries, 0 generals, 0 SOSs, and 0 indicators.\n",
      "Presolve time = 0.02 sec. (1.19 ticks)\n",
      "Represolve time = 0.03 sec. (10.43 ticks)\n",
      "Probing fixed 0 vars, tightened 6 bounds.\n",
      "Probing time = 0.00 sec. (1.06 ticks)\n",
      "Cover probing fixed 0 vars, tightened 283 bounds.\n",
      "Clique table members: 174.\n",
      "MIP emphasis: balance optimality and feasibility.\n",
      "MIP search method: dynamic search.\n",
      "Parallel mode: deterministic, using up to 8 threads.\n",
      "Root relaxation solution time = 0.02 sec. (8.33 ticks)\n",
      "\n",
      "        Nodes                                         Cuts/\n",
      "   Node  Left     Objective  IInf  Best Integer    Best Bound    ItCnt     Gap\n",
      "\n",
      "*     0+    0                          205.0000      195.0000             4.88%\n",
      "      0     0      195.0000    50      205.0000      195.0000     2793    4.88%\n",
      "*     0+    0                          197.0000      195.0000             1.02%\n",
      "      0     0        cutoff            197.0000      197.0000     2998    0.00%\n",
      "Elapsed time = 1.06 sec. (721.34 ticks, tree = 0.01 MB, solutions = 4)\n",
      "\n",
      "Cover cuts applied:  5\n",
      "Implied bound cuts applied:  5\n",
      "Flow cuts applied:  38\n",
      "Mixed integer rounding cuts applied:  1\n",
      "Zero-half cuts applied:  2\n",
      "Gomory fractional cuts applied:  2\n",
      "\n",
      "Root node processing (before b&c):\n",
      "  Real time             =    1.08 sec. (721.58 ticks)\n",
      "Parallel b&c, 8 threads:\n",
      "  Real time             =    0.00 sec. (0.00 ticks)\n",
      "  Sync time (average)   =    0.00 sec.\n",
      "  Wait time (average)   =    0.00 sec.\n",
      "                          ------------\n",
      "Total (root+branch&cut) =    1.08 sec. (721.58 ticks)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective Value: 197\n"
     ]
    }
   ],
   "source": [
    "solution = mdl.solve(log_output=True) # Solve the TSP problem\n",
    "#print(mdl.export_to_string()) # Export the model to String\n",
    "valor_objetivo = mdl.solution.get_objective_value() # Get the objective value of the final solution\n",
    "x=solution.get_value_dict(x, keep_zeros=True, precision=1) # Update the 'x' dictionary with zero-valued variables\n",
    "graphic(x,C,N) # Visualize the final solution\n",
    "print(\"Objective Value:\",valor_objetivo) # Print the objective value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d44fd4",
   "metadata": {},
   "source": [
    "#### Literature \n",
    "\n",
    "Gavish B, Graves S (1978) The travelling salesman problem and related problems, working Paper GR-078-78. Operations Research Center, Massachusetts Institute of Technology, Cambridge\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64e6c86e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}