Update MultipleKnapsack[Mip|Sat]
This commit is contained in:
Corentin Le Molgat
committed by
Mizux Seiha
Mizux Seiha
parent
628ea0465c
commit
1d48a794d4
@@ -12,6 +12,7 @@
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// limitations under the License.
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// [START program]
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// Solves a multiple knapsack problem using the CP-SAT solver.
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package com.google.ortools.sat.samples;
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// [START import]
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import com.google.ortools.Loader;
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@@ -20,118 +21,105 @@ import com.google.ortools.sat.CpSolver;
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import com.google.ortools.sat.CpSolverStatus;
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import com.google.ortools.sat.IntVar;
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import com.google.ortools.sat.LinearExpr;
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import java.util.stream.IntStream;
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// [END import]
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/** Sample showing how to solve a multiple knapsack problem. */
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public class MultipleKnapsackSat {
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// [START data]
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static class DataModel {
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int[] items = new int[] {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
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int[] values = new int[] {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
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int[] binCapacities = new int[] {100, 100, 100, 100, 100};
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int numItems = items.length;
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int numBins = 5;
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}
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// [END data]
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// [START solution_printer]
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static void printSolution(
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DataModel data, CpSolver solver, IntVar[][] x, IntVar[] load, IntVar[] value) {
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System.out.printf("Optimal objective value: %f%n", solver.objectiveValue());
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System.out.println();
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long packedWeight = 0;
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long packedValue = 0;
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for (int b = 0; b < data.numBins; ++b) {
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System.out.println("Bin " + b);
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for (int i = 0; i < data.numItems; ++i) {
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if (solver.value(x[i][b]) > 0) {
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System.out.println(
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"Item " + i + " - Weight: " + data.items[i] + " Value: " + data.values[i]);
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}
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}
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System.out.println("Packed bin weight: " + solver.value(load[b]));
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packedWeight = packedWeight + solver.value(load[b]);
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System.out.println("Packed bin value: " + solver.value(value[b]) + "\n");
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packedValue = packedValue + solver.value(value[b]);
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}
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System.out.println("Total packed weight: " + packedWeight);
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System.out.println("Total packed value: " + packedValue);
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}
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public static void main(String[] args) {
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Loader.loadNativeLibraries();
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// Instantiate the data problem.
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// [START data]
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final DataModel data = new DataModel();
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final int[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
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final int[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
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final int numItems = weights.length;
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final int[] allItems = IntStream.range(0, numItems).toArray();
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final int[] binCapacities = {100, 100, 100, 100, 100};
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final int numBins = binCapacities.length;
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final int[] allBins = IntStream.range(0, numBins).toArray();
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// [END data]
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int totalValue = 0;
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for (int i = 0; i < data.numItems; ++i) {
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totalValue = totalValue + data.values[i];
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}
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// [START model]
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CpModel model = new CpModel();
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// [END model]
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// Variables.
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// [START variables]
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IntVar[][] x = new IntVar[data.numItems][data.numBins];
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for (int i = 0; i < data.numItems; ++i) {
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for (int b = 0; b < data.numBins; ++b) {
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x[i][b] = model.newIntVar(0, 1, "x_" + i + "_" + b);
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IntVar[][] x = new IntVar[numItems][numBins];
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for (int i : allItems) {
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for (int b : allBins) {
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x[i][b] = model.newBoolVar("x_" + i + "_" + b);
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}
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}
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// Main variables.
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// Load and value variables.
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IntVar[] load = new IntVar[data.numBins];
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IntVar[] value = new IntVar[data.numBins];
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for (int b = 0; b < data.numBins; ++b) {
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load[b] = model.newIntVar(0, data.binCapacities[b], "load_" + b);
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value[b] = model.newIntVar(0, totalValue, "value_" + b);
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}
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// Links load and value with x.
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int[] sizes = new int[data.numItems];
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for (int i = 0; i < data.numItems; ++i) {
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sizes[i] = data.items[i];
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}
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for (int b = 0; b < data.numBins; ++b) {
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IntVar[] vars = new IntVar[data.numItems];
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for (int i = 0; i < data.numItems; ++i) {
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vars[i] = x[i][b];
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}
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model.addEquality(LinearExpr.scalProd(vars, data.items), load[b]);
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model.addEquality(LinearExpr.scalProd(vars, data.values), value[b]);
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}
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// [END variables]
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// Constraints.
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// [START constraints]
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// Each item can be in at most one bin.
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// Place all items.
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for (int i = 0; i < data.numItems; ++i) {
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IntVar[] vars = new IntVar[data.numBins];
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for (int b = 0; b < data.numBins; ++b) {
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// Each item is assigned to at most one bin.
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for (int i : allItems) {
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IntVar[] vars = new IntVar[numBins];
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for (int b : allBins) {
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vars[b] = x[i][b];
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}
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model.addLessOrEqual(LinearExpr.sum(vars), 1);
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}
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// The amount packed in each bin cannot exceed its capacity.
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for (int b : allBins) {
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IntVar[] binWeights = new IntVar[numItems];
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for (int i : allItems) {
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binWeights[i] = LinearExpr.term(x[i][b], weights[i]);
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}
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model.addLessOrEqual(LinearExpr.sum(binWeights), 1);
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}
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// [END constraints]
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// Maximize sum of load.
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// Objective.
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// [START objective]
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model.maximize(LinearExpr.sum(value));
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// Maximize total value of packed items.
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IntVar[] objective = new IntVar[numItems * numBins];
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for (int i : allItems) {
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for (int b : allBins) {
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int k = i * numBins + b;
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objective[k] = LinearExpr.term(x[i][b], values[i]);
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}
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}
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model.maximize(LinearExpr.sum(objective));
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// [END objective]
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// [START solve]
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CpSolver solver = new CpSolver();
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CpSolverStatus status = solver.solve(model);
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final CpSolverStatus status = solver.solve(model);
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// [END solve]
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// [START print_solution]
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System.out.println("Solve status: " + status);
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// Check that the problem has an optimal solution.
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if (status == CpSolverStatus.OPTIMAL) {
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printSolution(data, solver, x, load, value);
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System.out.println("Total packed value: " + solver.objectiveValue());
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long totalWeight = 0;
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for (int b : allBins) {
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long binWeight = 0;
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long binValue = 0;
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System.out.println("Bin " + b);
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for (int i : allItems) {
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if (solver.value(x[i][b]) > 0) {
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System.out.println("Item " + i + " weight: " + weights[i] + " value: " + values[i]);
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binWeight += weights[i];
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binValue += values[i];
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}
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}
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System.out.println("Packed bin weight: " + binWeight);
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System.out.println("Packed bin value: " + binValue);
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totalWeight += binWeight;
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}
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System.out.println("Total packed weight: " + totalWeight);
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} else {
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System.err.println("The problem does not have an optimal solution.");
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}
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// [END print_solution]
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}
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private MultipleKnapsackSat() {}
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}
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// [END program]
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