TSP Miller–Tucker–Zemlin (MTZ) callbacks formulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13f77eb7",
   "metadata": {},
   "source": [
    "## TSP Miller–Tucker–Zemlin (MTZ) Callbacks Formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1136a808",
   "metadata": {},
   "source": [
    "##### Keywords:  TSP, MTZ, Miller–Tucker–Zemlin, Subtours, Linear Programming, Cplex, Python, Networkx, Docplex, Callback, LazyConstraintCallback  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504cfd45",
   "metadata": {},
   "source": [
    "$ \\text{Variables} $\n",
    "\n",
    "$x_{ij}={\\begin{cases}1&{\\text{the path goes from city }}i{\\text{ to city }}j\\\\0&{\\text{otherwise}}\\end{cases}}$\n",
    "\n",
    "$u_{i}=\\text{keeps track of the order in which the cities are visited, counting from city 1 the interpretation is that $u_{i}$ < $u_{j}$ implies city i}\\\\ \\quad ~~~~\\text{is visited before city j}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258b2eeb",
   "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",
    "& & u_{i} - u_{j} + n x_{ij} \\leq n - 1, \\quad 2 \\leq i \\neq j \\leq n.\\\\\n",
    "&\n",
    "& & x_{ij} \\in \\{0,1\\}, \\quad i,j = 1,\\ldots,n, \\, i\\neq j, \\\\\n",
    "&\n",
    "& & u_{i} \\in \\mathbf {Z}, \\quad i=2,\\ldots ,n.\n",
    "\\end{aligned}\n",
    "\\end{equation*}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b93ca8",
   "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('MTZ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f28dd6",
   "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": "71bf617b",
   "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": "82339470",
   "metadata": {},
   "source": [
    "#### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c91a8114",
   "metadata": {},
   "outputs": [],
   "source": [
    "n=len(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd3f198",
   "metadata": {},
   "source": [
    "#### Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d76d9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "N=[i+1 for i in range(n)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b61b670",
   "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}\n",
    "Ind2={(i) for i in N if i!=1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16099e66",
   "metadata": {},
   "source": [
    "#### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36faf360",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=mdl.binary_var_dict(Ind, name='x')\n",
    "u=mdl.integer_var_dict(Ind2, name='u')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46118a9a",
   "metadata": {},
   "source": [
    "#### Callbacks LazyCuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "514b40c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cplex.callbacks import LazyConstraintCallback\n",
    "from docplex.mp.callbacks.cb_mixin import *\n",
    "from docplex.mp.model import Model\n",
    "\n",
    "class CustomLazyCallback(ConstraintCallbackMixin, LazyConstraintCallback):\n",
    "    \"\"\"\n",
    "    Custom lazy constraint callback class for CPLEX and docplex.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, env):\n",
    "        \"\"\"\n",
    "        Constructor for the CustomLazyCallback class.\n",
    "\n",
    "        Args:\n",
    "            env: The CPLEX environment.\n",
    "\n",
    "        \"\"\"\n",
    "        LazyConstraintCallback.__init__(self, env)  # Initialize the parent LazyConstraintCallback\n",
    "        ConstraintCallbackMixin.__init__(self)  # Initialize the parent ConstraintCallbackMixin\n",
    "        self.nb_lazy_cts = 0  # Initialize the number of lazy constraints added to 0\n",
    "\n",
    "    def add_lazy_constraints(self, cts):\n",
    "        \"\"\"\n",
    "        Method to add a list of lazy constraints to the callback.\n",
    "\n",
    "        Args:\n",
    "            cts: List of constraints to be added as lazy constraints.\n",
    "\n",
    "        \"\"\"\n",
    "        self.register_constraints(cts)  # Register the constraints to the callback\n",
    "\n",
    "    @print_called('--> lazy constraint callback called: #{0}')\n",
    "    def __call__(self):\n",
    "        \"\"\"\n",
    "        Method called when the lazy constraint callback is triggered.\n",
    "\n",
    "        \"\"\"\n",
    "        sol = self.make_solution()  # Obtain the current solution\n",
    "        unsats = self.get_cpx_unsatisfied_cts(self.cts, sol, tolerance=1e-6)  # Get the unsatisfied constraints\n",
    "        for ct, cpx_lhs, sense, cpx_rhs in unsats:\n",
    "            self.add(cpx_lhs, sense, cpx_rhs)  # Add the unsatisfied constraint to the model\n",
    "            self.nb_lazy_cts += 1  # Increment the number of lazy constraints added\n",
    "            print('  -- new lazy constraint[{0}]: {1!s}'.format(self.nb_lazy_cts, ct))  # Print information about the added constraint\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65161ea9",
   "metadata": {},
   "source": [
    "#### Mathematical Model of Traveling Salesman Problem MTZ LazyCallbacks (TSP MTZ LazyCallbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830c10ee",
   "metadata": {},
   "source": [
    "#### Objective Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": "6117c9f3",
   "metadata": {},
   "source": [
    "#### Subject to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "id": "deed687d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(2,n+1):\n",
    "#     for j in range(2,n+1):\n",
    "#         if i!=j:\n",
    "#             if i!=1:\n",
    "#                 mdl.add_constraint(u[i]-u[j]+(n-1)*x[i,j]<=(n-2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a73d39",
   "metadata": {},
   "source": [
    "#### Solve the Traveling Salesman Problem MTZ with LazyCallbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85255c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "lazyct_cb = mdl.register_callback(CustomLazyCallback)  # Register the CustomLazyCallback callback\n",
    "mdl.lazy_callback = lazyct_cb  # Set the lazy callback for the model\n",
    "\n",
    "lazyct_cb.add_lazy_constraints(u[i] - u[j] + (n - 1) * x[i, j] <= (n - 2) for i in range(2, n + 1) for j in range(2, n + 1) if i != j)\n",
    "# Add lazy constraints to the callback using a generator expression.\n",
    "# Each constraint is of the form u[i] - u[j] + (n - 1) * x[i, j] <= (n - 2),\n",
    "# where i and j range from 2 to n, and i is not equal to j.\n",
    "\n",
    "mdl.parameters.preprocessing.presolve = 0\n",
    "# Set the presolve parameter to 0 to disable preprocessing in the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb989c70",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version identifier: 12.10.0.0 | 2019-11-26 | 843d4de2ae\n",
      "CPXPARAM_Preprocessing_Presolve                  0\n",
      "CPXPARAM_Read_DataCheck                          1\n",
      "CPXPARAM_RandomSeed                              201903125\n",
      "Warning: Control callbacks may disable some MIP features.\n",
      "Lazy constraint(s) or lazy constraint/branch callback is present.\n",
      "    Disabling dual reductions (CPX_PARAM_REDUCE) in presolve.\n",
      "    Disabling non-linear reductions (CPX_PARAM_PRELINEAR) in presolve.\n",
      "    Disabling presolve reductions that prevent crushing forms.\n",
      "Clique table members: 100.\n",
      "MIP emphasis: balance optimality and feasibility.\n",
      "MIP search method: traditional branch-and-cut.\n",
      "Parallel mode: none, using 1 thread.\n",
      "Root relaxation solution time = 0.00 sec. (0.59 ticks)\n",
      "--> lazy constraint callback called: #1\n",
      "  -- new lazy constraint[1]: 49x_2_33+u_2-u_33 <= 48\n",
      "  -- new lazy constraint[2]: 49x_3_21+u_3-u_21 <= 48\n",
      "  -- new lazy constraint[3]: 49x_4_42+u_4-u_42 <= 48\n",
      "  -- new lazy constraint[4]: 49x_5_28+u_5-u_28 <= 48\n",
      "  -- new lazy constraint[5]: 49x_6_7+u_6-u_7 <= 48\n",
      "  -- new lazy constraint[6]: 49x_7_24+u_7-u_24 <= 48\n",
      "  -- new lazy constraint[7]: 49x_8_9+u_8-u_9 <= 48\n",
      "  -- new lazy constraint[8]: 49x_9_44+u_9-u_44 <= 48\n",
      "  -- new lazy constraint[9]: 49x_10_32+u_10-u_32 <= 48\n",
      "  -- new lazy constraint[10]: 49x_11_13+u_11-u_13 <= 48\n",
      "  -- new lazy constraint[11]: 49x_12_3-u_3+u_12 <= 48\n",
      "  -- new lazy constraint[12]: 49x_13_50+u_13-u_50 <= 48\n",
      "  -- new lazy constraint[13]: 49x_14_45+u_14-u_45 <= 48\n",
      "  -- new lazy constraint[14]: 49x_15_25+u_15-u_25 <= 48\n",
      "  -- new lazy constraint[15]: 49x_16_36+u_16-u_36 <= 48\n",
      "  -- new lazy constraint[16]: 49x_17_37+u_17-u_37 <= 48\n",
      "  -- new lazy constraint[17]: 49x_18_23+u_18-u_23 <= 48\n",
      "  -- new lazy constraint[18]: 49x_19_40+u_19-u_40 <= 48\n",
      "  -- new lazy constraint[19]: 49x_20_26+u_20-u_26 <= 48\n",
      "  -- new lazy constraint[20]: 49x_21_4-u_4+u_21 <= 48\n",
      "  -- new lazy constraint[21]: 49x_22_18-u_18+u_22 <= 48\n",
      "  -- new lazy constraint[22]: 49x_23_27+u_23-u_27 <= 48\n",
      "  -- new lazy constraint[23]: 49x_24_10-u_10+u_24 <= 48\n",
      "  -- new lazy constraint[24]: 49x_25_39+u_25-u_39 <= 48\n",
      "  -- new lazy constraint[25]: 49x_26_47+u_26-u_47 <= 48\n",
      "  -- new lazy constraint[26]: 49x_27_22-u_22+u_27 <= 48\n",
      "  -- new lazy constraint[27]: 49x_28_38+u_28-u_38 <= 48\n",
      "  -- new lazy constraint[28]: 49x_29_6-u_6+u_29 <= 48\n",
      "  -- new lazy constraint[29]: 49x_30_20-u_20+u_30 <= 48\n",
      "  -- new lazy constraint[30]: 49x_32_30-u_30+u_32 <= 48\n",
      "  -- new lazy constraint[31]: 49x_33_16-u_16+u_33 <= 48\n",
      "  -- new lazy constraint[32]: 49x_34_5-u_5+u_34 <= 48\n",
      "  -- new lazy constraint[33]: 49x_35_48+u_35-u_48 <= 48\n",
      "  -- new lazy constraint[34]: 49x_36_46+u_36-u_46 <= 48\n",
      "  -- new lazy constraint[35]: 49x_37_14-u_14+u_37 <= 48\n",
      "  -- new lazy constraint[36]: 49x_38_31-u_31+u_38 <= 48\n",
      "  -- new lazy constraint[37]: 49x_39_34-u_34+u_39 <= 48\n",
      "  -- new lazy constraint[38]: 49x_40_19-u_19+u_40 <= 48\n",
      "  -- new lazy constraint[39]: 49x_41_43+u_41-u_43 <= 48\n",
      "  -- new lazy constraint[40]: 49x_42_15-u_15+u_42 <= 48\n",
      "  -- new lazy constraint[41]: 49x_43_17-u_17+u_43 <= 48\n",
      "  -- new lazy constraint[42]: 49x_44_35-u_35+u_44 <= 48\n",
      "  -- new lazy constraint[43]: 49x_45_11-u_11+u_45 <= 48\n",
      "  -- new lazy constraint[44]: 49x_46_12-u_12+u_46 <= 48\n",
      "  -- new lazy constraint[45]: 49x_47_2-u_2+u_47 <= 48\n",
      "  -- new lazy constraint[46]: 49x_48_41-u_41+u_48 <= 48\n",
      "  -- new lazy constraint[47]: 49x_49_29-u_29+u_49 <= 48\n",
      "  -- new lazy constraint[48]: 49x_50_8-u_8+u_50 <= 48\n",
      "\n",
      "        Nodes                                         Cuts/\n",
      "   Node  Left     Objective  IInf  Best Integer    Best Bound    ItCnt     Gap         Variable B NodeID Parent  Depth\n",
      "\n",
      "      0     0      188.6122    23                    188.0000       36         \n",
      "      0     0      196.1429    23                     Cuts: 9       44         \n",
      "--> lazy constraint callback called: #2\n",
      "  -- new lazy constraint[49]: 49x_8_19+u_8-u_19 <= 48\n",
      "  -- new lazy constraint[50]: 49x_11_8-u_8+u_11 <= 48\n",
      "  -- new lazy constraint[51]: 49x_35_2-u_2+u_35 <= 48\n",
      "  -- new lazy constraint[52]: 49x_35_5-u_5+u_35 <= 48\n",
      "  -- new lazy constraint[53]: 49x_35_6-u_6+u_35 <= 48\n",
      "  -- new lazy constraint[54]: 49x_35_7-u_7+u_35 <= 48\n",
      "  -- new lazy constraint[55]: 49x_35_8-u_8+u_35 <= 48\n",
      "  -- new lazy constraint[56]: 49x_35_9-u_9+u_35 <= 48\n",
      "  -- new lazy constraint[57]: 49x_35_10-u_10+u_35 <= 48\n",
      "  -- new lazy constraint[58]: 49x_35_13-u_13+u_35 <= 48\n",
      "  -- new lazy constraint[59]: 49x_35_16-u_16+u_35 <= 48\n",
      "  -- new lazy constraint[60]: 49x_35_18-u_18+u_35 <= 48\n",
      "  -- new lazy constraint[61]: 49x_35_19-u_19+u_35 <= 48\n",
      "  -- new lazy constraint[62]: 49x_35_20-u_20+u_35 <= 48\n",
      "  -- new lazy constraint[63]: 49x_35_22-u_22+u_35 <= 48\n",
      "  -- new lazy constraint[64]: 49x_35_23-u_23+u_35 <= 48\n",
      "  -- new lazy constraint[65]: 49x_35_24-u_24+u_35 <= 48\n",
      "  -- new lazy constraint[66]: 49x_35_26-u_26+u_35 <= 48\n",
      "  -- new lazy constraint[67]: 49x_35_27-u_27+u_35 <= 48\n",
      "  -- new lazy constraint[68]: 49x_35_28-u_28+u_35 <= 48\n",
      "  -- new lazy constraint[69]: 49x_35_29-u_29+u_35 <= 48\n",
      "  -- new lazy constraint[70]: 49x_35_30-u_30+u_35 <= 48\n",
      "  -- new lazy constraint[71]: 49x_35_31-u_31+u_35 <= 48\n",
      "  -- new lazy constraint[72]: 49x_35_32-u_32+u_35 <= 48\n",
      "  -- new lazy constraint[73]: 49x_35_33-u_33+u_35 <= 48\n",
      "  -- new lazy constraint[74]: 49x_35_34-u_34+u_35 <= 48\n",
      "  -- new lazy constraint[75]: 49x_35_36+u_35-u_36 <= 48\n",
      "  -- new lazy constraint[76]: 49x_35_38+u_35-u_38 <= 48\n",
      "  -- new lazy constraint[77]: 49x_35_40+u_35-u_40 <= 48\n",
      "  -- new lazy constraint[78]: 49x_35_46+u_35-u_46 <= 48\n",
      "  -- new lazy constraint[79]: 49x_35_47+u_35-u_47 <= 48\n",
      "  -- new lazy constraint[80]: 49x_35_49+u_35-u_49 <= 48\n",
      "  -- new lazy constraint[81]: 49x_35_50+u_35-u_50 <= 48\n",
      "  -- new lazy constraint[82]: 49x_39_22-u_22+u_39 <= 48\n",
      "  -- new lazy constraint[83]: 49x_40_13-u_13+u_40 <= 48\n",
      "  -- new lazy constraint[84]: 49x_41_2-u_2+u_41 <= 48\n",
      "  -- new lazy constraint[85]: 49x_41_3-u_3+u_41 <= 48\n",
      "  -- new lazy constraint[86]: 49x_41_5-u_5+u_41 <= 48\n",
      "  -- new lazy constraint[87]: 49x_41_6-u_6+u_41 <= 48\n",
      "  -- new lazy constraint[88]: 49x_41_7-u_7+u_41 <= 48\n",
      "  -- new lazy constraint[89]: 49x_41_8-u_8+u_41 <= 48\n",
      "  -- new lazy constraint[90]: 49x_41_9-u_9+u_41 <= 48\n",
      "  -- new lazy constraint[91]: 49x_41_10-u_10+u_41 <= 48\n",
      "  -- new lazy constraint[92]: 49x_41_12-u_12+u_41 <= 48\n",
      "  -- new lazy constraint[93]: 49x_41_13-u_13+u_41 <= 48\n",
      "  -- new lazy constraint[94]: 49x_41_16-u_16+u_41 <= 48\n",
      "  -- new lazy constraint[95]: 49x_41_18-u_18+u_41 <= 48\n",
      "  -- new lazy constraint[96]: 49x_41_19-u_19+u_41 <= 48\n",
      "  -- new lazy constraint[97]: 49x_41_20-u_20+u_41 <= 48\n",
      "  -- new lazy constraint[98]: 49x_41_22-u_22+u_41 <= 48\n",
      "  -- new lazy constraint[99]: 49x_41_23-u_23+u_41 <= 48\n",
      "  -- new lazy constraint[100]: 49x_41_24-u_24+u_41 <= 48\n",
      "  -- new lazy constraint[101]: 49x_41_26-u_26+u_41 <= 48\n",
      "  -- new lazy constraint[102]: 49x_41_27-u_27+u_41 <= 48\n",
      "  -- new lazy constraint[103]: 49x_41_28-u_28+u_41 <= 48\n",
      "  -- new lazy constraint[104]: 49x_41_29-u_29+u_41 <= 48\n",
      "  -- new lazy constraint[105]: 49x_41_30-u_30+u_41 <= 48\n",
      "  -- new lazy constraint[106]: 49x_41_31-u_31+u_41 <= 48\n",
      "  -- new lazy constraint[107]: 49x_41_32-u_32+u_41 <= 48\n",
      "  -- new lazy constraint[108]: 49x_41_33-u_33+u_41 <= 48\n",
      "  -- new lazy constraint[109]: 49x_41_34-u_34+u_41 <= 48\n",
      "  -- new lazy constraint[110]: 49x_41_36-u_36+u_41 <= 48\n",
      "  -- new lazy constraint[111]: 49x_41_38-u_38+u_41 <= 48\n",
      "  -- new lazy constraint[112]: 49x_41_40-u_40+u_41 <= 48\n",
      "  -- new lazy constraint[113]: 49x_41_46+u_41-u_46 <= 48\n",
      "  -- new lazy constraint[114]: 49x_41_47+u_41-u_47 <= 48\n",
      "  -- new lazy constraint[115]: 49x_41_49+u_41-u_49 <= 48\n",
      "  -- new lazy constraint[116]: 49x_41_50+u_41-u_50 <= 48\n",
      "  -- new lazy constraint[117]: 49x_44_2-u_2+u_44 <= 48\n",
      "  -- new lazy constraint[118]: 49x_44_5-u_5+u_44 <= 48\n",
      "  -- new lazy constraint[119]: 49x_44_6-u_6+u_44 <= 48\n",
      "  -- new lazy constraint[120]: 49x_44_7-u_7+u_44 <= 48\n",
      "  -- new lazy constraint[121]: 49x_44_8-u_8+u_44 <= 48\n",
      "  -- new lazy constraint[122]: 49x_44_9-u_9+u_44 <= 48\n",
      "  -- new lazy constraint[123]: 49x_44_10-u_10+u_44 <= 48\n",
      "  -- new lazy constraint[124]: 49x_44_13-u_13+u_44 <= 48\n",
      "  -- new lazy constraint[125]: 49x_44_16-u_16+u_44 <= 48\n",
      "  -- new lazy constraint[126]: 49x_44_18-u_18+u_44 <= 48\n",
      "  -- new lazy constraint[127]: 49x_44_19-u_19+u_44 <= 48\n",
      "  -- new lazy constraint[128]: 49x_44_20-u_20+u_44 <= 48\n",
      "  -- new lazy constraint[129]: 49x_44_22-u_22+u_44 <= 48\n",
      "  -- new lazy constraint[130]: 49x_44_23-u_23+u_44 <= 48\n",
      "  -- new lazy constraint[131]: 49x_44_24-u_24+u_44 <= 48\n",
      "  -- new lazy constraint[132]: 49x_44_26-u_26+u_44 <= 48\n",
      "  -- new lazy constraint[133]: 49x_44_27-u_27+u_44 <= 48\n",
      "  -- new lazy constraint[134]: 49x_44_28-u_28+u_44 <= 48\n",
      "  -- new lazy constraint[135]: 49x_44_29-u_29+u_44 <= 48\n",
      "  -- new lazy constraint[136]: 49x_44_30-u_30+u_44 <= 48\n",
      "  -- new lazy constraint[137]: 49x_44_31-u_31+u_44 <= 48\n",
      "  -- new lazy constraint[138]: 49x_44_32-u_32+u_44 <= 48\n",
      "  -- new lazy constraint[139]: 49x_44_33-u_33+u_44 <= 48\n",
      "  -- new lazy constraint[140]: 49x_44_34-u_34+u_44 <= 48\n",
      "  -- new lazy constraint[141]: 49x_44_36-u_36+u_44 <= 48\n",
      "  -- new lazy constraint[142]: 49x_44_38-u_38+u_44 <= 48\n",
      "  -- new lazy constraint[143]: 49x_44_40-u_40+u_44 <= 48\n",
      "  -- new lazy constraint[144]: 49x_44_47+u_44-u_47 <= 48\n",
      "  -- new lazy constraint[145]: 49x_44_49+u_44-u_49 <= 48\n",
      "  -- new lazy constraint[146]: 49x_44_50+u_44-u_50 <= 48\n",
      "  -- new lazy constraint[147]: 49x_48_2-u_2+u_48 <= 48\n",
      "  -- new lazy constraint[148]: 49x_48_5-u_5+u_48 <= 48\n",
      "  -- new lazy constraint[149]: 49x_48_6-u_6+u_48 <= 48\n",
      "  -- new lazy constraint[150]: 49x_48_7-u_7+u_48 <= 48\n",
      "  -- new lazy constraint[151]: 49x_48_8-u_8+u_48 <= 48\n",
      "  -- new lazy constraint[152]: 49x_48_9-u_9+u_48 <= 48\n",
      "  -- new lazy constraint[153]: 49x_48_10-u_10+u_48 <= 48\n",
      "  -- new lazy constraint[154]: 49x_48_12-u_12+u_48 <= 48\n",
      "  -- new lazy constraint[155]: 49x_48_13-u_13+u_48 <= 48\n",
      "  -- new lazy constraint[156]: 49x_48_16-u_16+u_48 <= 48\n",
      "  -- new lazy constraint[157]: 49x_48_18-u_18+u_48 <= 48\n",
      "  -- new lazy constraint[158]: 49x_48_19-u_19+u_48 <= 48\n",
      "  -- new lazy constraint[159]: 49x_48_20-u_20+u_48 <= 48\n",
      "  -- new lazy constraint[160]: 49x_48_22-u_22+u_48 <= 48\n",
      "  -- new lazy constraint[161]: 49x_48_23-u_23+u_48 <= 48\n",
      "  -- new lazy constraint[162]: 49x_48_24-u_24+u_48 <= 48\n",
      "  -- new lazy constraint[163]: 49x_48_26-u_26+u_48 <= 48\n",
      "  -- new lazy constraint[164]: 49x_48_27-u_27+u_48 <= 48\n",
      "  -- new lazy constraint[165]: 49x_48_28-u_28+u_48 <= 48\n",
      "  -- new lazy constraint[166]: 49x_48_29-u_29+u_48 <= 48\n",
      "  -- new lazy constraint[167]: 49x_48_30-u_30+u_48 <= 48\n",
      "  -- new lazy constraint[168]: 49x_48_31-u_31+u_48 <= 48\n",
      "  -- new lazy constraint[169]: 49x_48_32-u_32+u_48 <= 48\n",
      "  -- new lazy constraint[170]: 49x_48_33-u_33+u_48 <= 48\n",
      "  -- new lazy constraint[171]: 49x_48_34-u_34+u_48 <= 48\n",
      "  -- new lazy constraint[172]: 49x_48_36-u_36+u_48 <= 48\n",
      "  -- new lazy constraint[173]: 49x_48_38-u_38+u_48 <= 48\n",
      "  -- new lazy constraint[174]: 49x_48_40-u_40+u_48 <= 48\n",
      "  -- new lazy constraint[175]: 49x_48_46-u_46+u_48 <= 48\n",
      "  -- new lazy constraint[176]: 49x_48_47-u_47+u_48 <= 48\n",
      "  -- new lazy constraint[177]: 49x_48_49+u_48-u_49 <= 48\n",
      "  -- new lazy constraint[178]: 49x_48_50+u_48-u_50 <= 48\n",
      "  -- new lazy constraint[179]: 49x_50_34-u_34+u_50 <= 48\n",
      "      0     0      198.0000    23                     Cuts: 9       64         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     0      198.0000    23                    Cuts: 14       80         \n",
      "      0     0      198.0000    23                     Cuts: 4       93         \n",
      "      0     0      198.0000    23                 ZeroHalf: 6      110         \n",
      "--> lazy constraint callback called: #3\n",
      "  -- new lazy constraint[180]: 49x_3_41+u_3-u_41 <= 48\n",
      "  -- new lazy constraint[181]: 49x_4_18+u_4-u_18 <= 48\n",
      "  -- new lazy constraint[182]: 49x_5_20+u_5-u_20 <= 48\n",
      "  -- new lazy constraint[183]: 49x_7_15+u_7-u_15 <= 48\n",
      "  -- new lazy constraint[184]: 49x_9_43+u_9-u_43 <= 48\n",
      "  -- new lazy constraint[185]: 49x_11_36+u_11-u_36 <= 48\n",
      "  -- new lazy constraint[186]: 49x_13_48+u_13-u_48 <= 48\n",
      "  -- new lazy constraint[187]: 49x_14_39+u_14-u_39 <= 48\n",
      "  -- new lazy constraint[188]: 49x_16_47+u_16-u_47 <= 48\n",
      "  -- new lazy constraint[189]: 49x_17_42+u_17-u_42 <= 48\n",
      "  -- new lazy constraint[190]: 49x_21_5-u_5+u_21 <= 48\n",
      "  -- new lazy constraint[191]: 49x_22_9-u_9+u_22 <= 48\n",
      "  -- new lazy constraint[192]: 49x_25_24-u_24+u_25 <= 48\n",
      "  -- new lazy constraint[193]: 49x_26_35+u_26-u_35 <= 48\n",
      "  -- new lazy constraint[194]: 49x_30_44+u_30-u_44 <= 48\n",
      "  -- new lazy constraint[195]: 49x_34_45+u_34-u_45 <= 48\n",
      "  -- new lazy constraint[196]: 49x_35_37+u_35-u_37 <= 48\n",
      "  -- new lazy constraint[197]: 49x_41_21-u_21+u_41 <= 48\n",
      "  -- new lazy constraint[198]: 49x_42_4-u_4+u_42 <= 48\n",
      "  -- new lazy constraint[199]: 49x_49_28-u_28+u_49 <= 48\n",
      "  -- new lazy constraint[200]: 49x_50_29-u_29+u_50 <= 48\n",
      "--> lazy constraint callback called: #4\n",
      "  -- new lazy constraint[201]: 49x_17_35+u_17-u_35 <= 48\n",
      "  -- new lazy constraint[202]: 49x_19_18-u_18+u_19 <= 48\n",
      "  -- new lazy constraint[203]: 49x_22_40+u_22-u_40 <= 48\n",
      "  -- new lazy constraint[204]: 49x_44_37-u_37+u_44 <= 48\n",
      "--> lazy constraint callback called: #5\n",
      "  -- new lazy constraint[205]: 49x_5_6+u_5-u_6 <= 48\n",
      "  -- new lazy constraint[206]: 49x_5_7+u_5-u_7 <= 48\n",
      "  -- new lazy constraint[207]: 49x_5_10+u_5-u_10 <= 48\n",
      "  -- new lazy constraint[208]: 49x_5_15+u_5-u_15 <= 48\n",
      "  -- new lazy constraint[209]: 49x_5_24+u_5-u_24 <= 48\n",
      "  -- new lazy constraint[210]: 49x_5_25+u_5-u_25 <= 48\n",
      "  -- new lazy constraint[211]: 49x_5_29+u_5-u_29 <= 48\n",
      "  -- new lazy constraint[212]: 49x_5_30+u_5-u_30 <= 48\n",
      "  -- new lazy constraint[213]: 49x_5_32+u_5-u_32 <= 48\n",
      "  -- new lazy constraint[214]: 49x_5_39+u_5-u_39 <= 48\n",
      "  -- new lazy constraint[215]: 49x_5_42+u_5-u_42 <= 48\n",
      "  -- new lazy constraint[216]: 49x_5_49+u_5-u_49 <= 48\n",
      "  -- new lazy constraint[217]: 49x_28_6-u_6+u_28 <= 48\n",
      "  -- new lazy constraint[218]: 49x_28_7-u_7+u_28 <= 48\n",
      "  -- new lazy constraint[219]: 49x_28_10-u_10+u_28 <= 48\n",
      "  -- new lazy constraint[220]: 49x_28_15-u_15+u_28 <= 48\n",
      "  -- new lazy constraint[221]: 49x_28_20-u_20+u_28 <= 48\n",
      "  -- new lazy constraint[222]: 49x_28_24-u_24+u_28 <= 48\n",
      "  -- new lazy constraint[223]: 49x_28_25-u_25+u_28 <= 48\n",
      "  -- new lazy constraint[224]: 49x_28_29+u_28-u_29 <= 48\n",
      "  -- new lazy constraint[225]: 49x_28_30+u_28-u_30 <= 48\n",
      "  -- new lazy constraint[226]: 49x_28_32+u_28-u_32 <= 48\n",
      "  -- new lazy constraint[227]: 49x_28_39+u_28-u_39 <= 48\n",
      "  -- new lazy constraint[228]: 49x_28_42+u_28-u_42 <= 48\n",
      "  -- new lazy constraint[229]: 49x_28_49+u_28-u_49 <= 48\n",
      "  -- new lazy constraint[230]: 49x_31_6-u_6+u_31 <= 48\n",
      "  -- new lazy constraint[231]: 49x_31_7-u_7+u_31 <= 48\n",
      "  -- new lazy constraint[232]: 49x_31_10-u_10+u_31 <= 48\n",
      "  -- new lazy constraint[233]: 49x_31_15-u_15+u_31 <= 48\n",
      "  -- new lazy constraint[234]: 49x_31_20-u_20+u_31 <= 48\n",
      "  -- new lazy constraint[235]: 49x_31_24-u_24+u_31 <= 48\n",
      "  -- new lazy constraint[236]: 49x_31_25-u_25+u_31 <= 48\n",
      "  -- new lazy constraint[237]: 49x_31_26-u_26+u_31 <= 48\n",
      "  -- new lazy constraint[238]: 49x_31_29-u_29+u_31 <= 48\n",
      "  -- new lazy constraint[239]: 49x_31_30-u_30+u_31 <= 48\n",
      "  -- new lazy constraint[240]: 49x_31_32+u_31-u_32 <= 48\n",
      "  -- new lazy constraint[241]: 49x_31_39+u_31-u_39 <= 48\n",
      "  -- new lazy constraint[242]: 49x_31_42+u_31-u_42 <= 48\n",
      "  -- new lazy constraint[243]: 49x_31_47+u_31-u_47 <= 48\n",
      "  -- new lazy constraint[244]: 49x_31_49+u_31-u_49 <= 48\n",
      "  -- new lazy constraint[245]: 49x_38_6-u_6+u_38 <= 48\n",
      "  -- new lazy constraint[246]: 49x_38_7-u_7+u_38 <= 48\n",
      "  -- new lazy constraint[247]: 49x_38_10-u_10+u_38 <= 48\n",
      "  -- new lazy constraint[248]: 49x_38_15-u_15+u_38 <= 48\n",
      "  -- new lazy constraint[249]: 49x_38_20-u_20+u_38 <= 48\n",
      "  -- new lazy constraint[250]: 49x_38_24-u_24+u_38 <= 48\n",
      "  -- new lazy constraint[251]: 49x_38_25-u_25+u_38 <= 48\n",
      "  -- new lazy constraint[252]: 49x_38_26-u_26+u_38 <= 48\n",
      "  -- new lazy constraint[253]: 49x_38_29-u_29+u_38 <= 48\n",
      "  -- new lazy constraint[254]: 49x_38_30-u_30+u_38 <= 48\n",
      "  -- new lazy constraint[255]: 49x_38_32-u_32+u_38 <= 48\n",
      "  -- new lazy constraint[256]: 49x_38_39+u_38-u_39 <= 48\n",
      "  -- new lazy constraint[257]: 49x_38_42+u_38-u_42 <= 48\n",
      "  -- new lazy constraint[258]: 49x_38_49+u_38-u_49 <= 48\n",
      "  -- new lazy constraint[259]: 49x_39_42+u_39-u_42 <= 48\n",
      "      0     2      198.0000    31                    198.0000      110                                 0             0\n",
      "Elapsed time = 0.41 sec. (167.91 ticks, tree = 0.01 MB, solutions = 0)\n",
      "--> lazy constraint callback called: #6\n",
      "  -- new lazy constraint[260]: 49x_5_14+u_5-u_14 <= 48\n",
      "  -- new lazy constraint[261]: 49x_5_17+u_5-u_17 <= 48\n",
      "  -- new lazy constraint[262]: 49x_5_37+u_5-u_37 <= 48\n",
      "  -- new lazy constraint[263]: 49x_5_43+u_5-u_43 <= 48\n",
      "  -- new lazy constraint[264]: 49x_5_45+u_5-u_45 <= 48\n",
      "  -- new lazy constraint[265]: 49x_23_2-u_2+u_23 <= 48\n",
      "  -- new lazy constraint[266]: 49x_23_3-u_3+u_23 <= 48\n",
      "  -- new lazy constraint[267]: 49x_23_4-u_4+u_23 <= 48\n",
      "  -- new lazy constraint[268]: 49x_23_6-u_6+u_23 <= 48\n",
      "  -- new lazy constraint[269]: 49x_23_7-u_7+u_23 <= 48\n",
      "  -- new lazy constraint[270]: 49x_23_10-u_10+u_23 <= 48\n",
      "  -- new lazy constraint[271]: 49x_23_12-u_12+u_23 <= 48\n",
      "  -- new lazy constraint[272]: 49x_23_14-u_14+u_23 <= 48\n",
      "  -- new lazy constraint[273]: 49x_23_15-u_15+u_23 <= 48\n",
      "  -- new lazy constraint[274]: 49x_23_16-u_16+u_23 <= 48\n",
      "  -- new lazy constraint[275]: 49x_23_17-u_17+u_23 <= 48\n",
      "  -- new lazy constraint[276]: 49x_23_20-u_20+u_23 <= 48\n",
      "  -- new lazy constraint[277]: 49x_23_21-u_21+u_23 <= 48\n",
      "  -- new lazy constraint[278]: 49x_23_24+u_23-u_24 <= 48\n",
      "  -- new lazy constraint[279]: 49x_23_25+u_23-u_25 <= 48\n",
      "  -- new lazy constraint[280]: 49x_23_26+u_23-u_26 <= 48\n",
      "  -- new lazy constraint[281]: 49x_23_29+u_23-u_29 <= 48\n",
      "  -- new lazy constraint[282]: 49x_23_30+u_23-u_30 <= 48\n",
      "  -- new lazy constraint[283]: 49x_23_32+u_23-u_32 <= 48\n",
      "  -- new lazy constraint[284]: 49x_23_33+u_23-u_33 <= 48\n",
      "  -- new lazy constraint[285]: 49x_23_36+u_23-u_36 <= 48\n",
      "  -- new lazy constraint[286]: 49x_23_37+u_23-u_37 <= 48\n",
      "  -- new lazy constraint[287]: 49x_23_42+u_23-u_42 <= 48\n",
      "  -- new lazy constraint[288]: 49x_23_43+u_23-u_43 <= 48\n",
      "  -- new lazy constraint[289]: 49x_23_45+u_23-u_45 <= 48\n",
      "  -- new lazy constraint[290]: 49x_23_46+u_23-u_46 <= 48\n",
      "  -- new lazy constraint[291]: 49x_23_47+u_23-u_47 <= 48\n",
      "  -- new lazy constraint[292]: 49x_23_49+u_23-u_49 <= 48\n",
      "  -- new lazy constraint[293]: 49x_27_2-u_2+u_27 <= 48\n",
      "  -- new lazy constraint[294]: 49x_27_3-u_3+u_27 <= 48\n",
      "  -- new lazy constraint[295]: 49x_27_4-u_4+u_27 <= 48\n",
      "  -- new lazy constraint[296]: 49x_27_6-u_6+u_27 <= 48\n",
      "  -- new lazy constraint[297]: 49x_27_7-u_7+u_27 <= 48\n",
      "  -- new lazy constraint[298]: 49x_27_10-u_10+u_27 <= 48\n",
      "  -- new lazy constraint[299]: 49x_27_12-u_12+u_27 <= 48\n",
      "  -- new lazy constraint[300]: 49x_27_14-u_14+u_27 <= 48\n",
      "  -- new lazy constraint[301]: 49x_27_15-u_15+u_27 <= 48\n",
      "  -- new lazy constraint[302]: 49x_27_16-u_16+u_27 <= 48\n",
      "  -- new lazy constraint[303]: 49x_27_17-u_17+u_27 <= 48\n",
      "  -- new lazy constraint[304]: 49x_27_20-u_20+u_27 <= 48\n",
      "  -- new lazy constraint[305]: 49x_27_21-u_21+u_27 <= 48\n",
      "  -- new lazy constraint[306]: 49x_27_24-u_24+u_27 <= 48\n",
      "  -- new lazy constraint[307]: 49x_27_25-u_25+u_27 <= 48\n",
      "  -- new lazy constraint[308]: 49x_27_26-u_26+u_27 <= 48\n",
      "  -- new lazy constraint[309]: 49x_27_29+u_27-u_29 <= 48\n",
      "  -- new lazy constraint[310]: 49x_27_30+u_27-u_30 <= 48\n",
      "  -- new lazy constraint[311]: 49x_27_32+u_27-u_32 <= 48\n",
      "  -- new lazy constraint[312]: 49x_27_33+u_27-u_33 <= 48\n",
      "  -- new lazy constraint[313]: 49x_27_36+u_27-u_36 <= 48\n",
      "  -- new lazy constraint[314]: 49x_27_37+u_27-u_37 <= 48\n",
      "  -- new lazy constraint[315]: 49x_27_39+u_27-u_39 <= 48\n",
      "  -- new lazy constraint[316]: 49x_27_42+u_27-u_42 <= 48\n",
      "  -- new lazy constraint[317]: 49x_27_43+u_27-u_43 <= 48\n",
      "  -- new lazy constraint[318]: 49x_27_45+u_27-u_45 <= 48\n",
      "  -- new lazy constraint[319]: 49x_27_46+u_27-u_46 <= 48\n",
      "  -- new lazy constraint[320]: 49x_27_47+u_27-u_47 <= 48\n",
      "  -- new lazy constraint[321]: 49x_27_49+u_27-u_49 <= 48\n",
      "  -- new lazy constraint[322]: 49x_28_14-u_14+u_28 <= 48\n",
      "  -- new lazy constraint[323]: 49x_28_17-u_17+u_28 <= 48\n",
      "  -- new lazy constraint[324]: 49x_28_37+u_28-u_37 <= 48\n",
      "  -- new lazy constraint[325]: 49x_28_43+u_28-u_43 <= 48\n",
      "  -- new lazy constraint[326]: 49x_28_45+u_28-u_45 <= 48\n",
      "  -- new lazy constraint[327]: 49x_31_14-u_14+u_31 <= 48\n",
      "  -- new lazy constraint[328]: 49x_31_17-u_17+u_31 <= 48\n",
      "  -- new lazy constraint[329]: 49x_31_37+u_31-u_37 <= 48\n",
      "  -- new lazy constraint[330]: 49x_31_43+u_31-u_43 <= 48\n",
      "  -- new lazy constraint[331]: 49x_31_45+u_31-u_45 <= 48\n",
      "  -- new lazy constraint[332]: 49x_34_14-u_14+u_34 <= 48\n",
      "  -- new lazy constraint[333]: 49x_34_17-u_17+u_34 <= 48\n",
      "  -- new lazy constraint[334]: 49x_34_37+u_34-u_37 <= 48\n",
      "  -- new lazy constraint[335]: 49x_34_43+u_34-u_43 <= 48\n",
      "  -- new lazy constraint[336]: 49x_35_17-u_17+u_35 <= 48\n",
      "  -- new lazy constraint[337]: 49x_35_43+u_35-u_43 <= 48\n",
      "  -- new lazy constraint[338]: 49x_38_14-u_14+u_38 <= 48\n",
      "  -- new lazy constraint[339]: 49x_38_17-u_17+u_38 <= 48\n",
      "  -- new lazy constraint[340]: 49x_38_37-u_37+u_38 <= 48\n",
      "  -- new lazy constraint[341]: 49x_38_43+u_38-u_43 <= 48\n",
      "  -- new lazy constraint[342]: 49x_38_45+u_38-u_45 <= 48\n",
      "  -- new lazy constraint[343]: 49x_44_43-u_43+u_44 <= 48\n",
      "  -- new lazy constraint[344]: 49x_50_17-u_17+u_50 <= 48\n",
      "  -- new lazy constraint[345]: 49x_50_37-u_37+u_50 <= 48\n",
      "  -- new lazy constraint[346]: 49x_50_43-u_43+u_50 <= 48\n",
      "--> lazy constraint callback called: #7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*     5     5      integral     0      200.0000      198.1633      214    0.92%\n",
      "\n",
      "Cover cuts applied:  2\n",
      "Zero-half cuts applied:  3\n",
      "Gomory fractional cuts applied:  3\n",
      "User cuts applied:  346\n",
      "\n",
      "Root node processing (before b&c):\n",
      "  Real time             =    0.41 sec. (167.93 ticks)\n",
      "Sequential b&c:\n",
      "  Real time             =    0.11 sec. (47.19 ticks)\n",
      "                          ------------\n",
      "Total (root+branch&cut) =    0.51 sec. (215.12 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: 200\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": "5a5f374c",
   "metadata": {},
   "source": [
    "#### Literature \n",
    "C. E. Miller, A. W. Tucker, and R. A. Zemlin. 1960. Integer Programming Formulation of Traveling Salesman Problems. J. ACM 7, 4 (Oct. 1960), 326–329. https://doi.org/10.1145/321043.321046\n",
    "\n",
    "    \n",
    "https://github.com/IBMDecisionOptimization/docplexexamples/tree/master/examples/mp/callbacks\n",
    "    \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"
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 },
 "nbformat": 4,
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