{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from typing import Sequence\n", "\n", "\n", "def find_subtours(x: Sequence[Sequence[int]]) -> Sequence[Sequence[int]]:\n", " \"\"\"\n", " Extracts subtours from the given 2D-array.\n", "\n", " Args:\n", " x: A two-dimensional array corresponding to the x variable in the TSP formulation, where\n", " x[i][j] is 1 if arc (i, j) is used.\n", "\n", " Returns:\n", " A list of subtours, where each subtour is a list.\n", " \"\"\"\n", " N = len(x)\n", " marked = [False] * N\n", " subtours: list[list[int]] = []\n", "\n", " while not all(marked):\n", " # Index of the first non-marked city:\n", " istart = min(range(N), key=lambda j: marked[j])\n", "\n", " # Create the subtour:\n", " subtour = [istart]\n", "\n", " while True:\n", " i = istart\n", " for i, b in enumerate(x[subtour[-1]]):\n", " if b:\n", " break\n", "\n", " if i == istart:\n", " break\n", "\n", " subtour.append(i)\n", "\n", " for i in subtour:\n", " marked[i] = True\n", "\n", " subtours.append(subtour)\n", "\n", " return subtours\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "699.0\n" ] } ], "source": [ "from docplex.mp.model import Model\n", "from docplex.mp.callbacks.cb_mixin import ConstraintCallbackMixin\n", "from cplex.callbacks import LazyConstraintCallback\n", "from docplex.mp.solution import SolveSolution\n", "import numpy as np\n", "from numpy.typing import NDArray\n", "\n", "import tsp.data\n", "\n", "\n", "class SubtourConstraintCallback(ConstraintCallbackMixin, LazyConstraintCallback):\n", " x: NDArray\n", "\n", " def __init__(self, env):\n", " LazyConstraintCallback.__init__(self, env)\n", " ConstraintCallbackMixin.__init__(self)\n", "\n", " self.nb_callback = 0\n", " self.nb_subtours = 0\n", "\n", " self.single_subtour_per_call = False\n", "\n", " def __call__(self):\n", " n = len(self.x)\n", "\n", " x_list = self.x.flatten().tolist()\n", "\n", " sol: SolveSolution = self.make_solution_from_vars(x_list)\n", "\n", " xvals = np.array(sol.get_values(x_list)).reshape(self.x.shape).astype(int)\n", " subtours = find_subtours(xvals)\n", "\n", " if len(subtours) == 1:\n", " return\n", "\n", " self.nb_callback += 1\n", "\n", " for subtour in subtours:\n", " xs = [\n", " self.x[i, j].index\n", " for i in subtour\n", " for j in range(n)\n", " if j not in subtour\n", " ]\n", " self.add([xs, [1] * len(xs)], \"G\", 1)\n", "\n", " self.nb_subtours += 1\n", "\n", " if self.single_subtour_per_call:\n", " break\n", "\n", "\n", "with Model(name=\"TSP\") as m:\n", " dist = tsp.data.grid42\n", " n = len(dist)\n", "\n", " x = np.array([m.binary_var_list(n, name=f\"x_{i}\") for i in range(n)])\n", "\n", " for i in range(n):\n", " m.add_constraint(x[i, :].sum() == 1)\n", " m.add_constraint(x[:, i].sum() == 1)\n", " m.add_constraint(x[i, i] == 0)\n", "\n", " m.set_objective(\"min\", (x * dist).sum())\n", "\n", " cb: SubtourConstraintCallback = m.register_callback(SubtourConstraintCallback)\n", " cb.x = x\n", "\n", " s = m.solve()\n", "\n", " print(cb.nb_subtours)\n", " print(s.get_objective_value())\n" ] } ], "metadata": { "kernelspec": { "display_name": "INSA", "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 2 }