b027e57e95
- Currently not implemented... Add abseil patch - Add patches/absl-config.cmake Makefile: Add abseil-cpp on unix - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake Makefile: Add abseil-cpp on windows - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake CMake: Add abseil-cpp - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake port to absl: C++ Part - Fix warning with the use of ABSL_MUST_USE_RESULT > The macro must appear as the very first part of a function declaration or definition: ... Note: past advice was to place the macro after the argument list. src: dependencies/sources/abseil-cpp-master/absl/base/attributes.h:418 - Rename enum after windows clash - Remove non compact table constraints - Change index type from int64 to int in routing library - Fix file_nonport compilation on windows - Fix another naming conflict with windows (NO_ERROR is a macro) - Cleanup hash containers; work on sat internals - Add optional_boolean sub-proto Sync cpp examples with internal code - reenable issue173 after reducing number of loops port to absl: Python Part - Add back cp_model.INT32_MIN|MAX for examples Update Python examples - Add random_tsp.py - Run words_square example - Run magic_square in python tests port to absl: Java Part - Fix compilation of the new routing parameters in java - Protect some code from SWIG parsing Update Java Examples port to absl: .Net Part Update .Net examples work on sat internals; Add C++ CP-SAT CpModelBuilder API; update sample code and recipes to use the new API; sync with internal code Remove VS 2015 in Appveyor-CI - abseil-cpp does not support VS 2015... improve tables upgrade C++ sat examples to use the new API; work on sat internals update license dates rewrite jobshop_ft06_distance.py to use the CP-SAT solver rename last example revert last commit more work on SAT internals fix
98 lines
3.1 KiB
Java
Executable File
98 lines
3.1 KiB
Java
Executable File
/*
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* Copyright 2017 Darian Sastre darian.sastre@minimaxlabs.com
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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* ************************************************************************
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*
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* Each knapsack perceives a different weight for each item. Item values are
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* the same across knapsacks. Optimizing constrains the count of each item such
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* that all knapsack capacities are respected, and their values are maximized.
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*
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* This model was created by Hakan Kjellerstrand (hakank@gmail.com)
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*/
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import com.google.ortools.linearsolver.*;
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public class KnapsackMIP {
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static {
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System.loadLibrary("jniortools");
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}
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private static MPSolver createSolver(String solverType) {
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try {
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return new MPSolver("MIPDiet", MPSolver.OptimizationProblemType.valueOf(solverType));
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} catch (java.lang.IllegalArgumentException e) {
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System.err.println("Bad solver type: " + e);
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return null;
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}
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}
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private static void solve(String solverType) {
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MPSolver solver = createSolver(solverType);
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/** variables */
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int itemCount = 12;
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int capacityCount = 7;
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int[] capacity = {18209, 7692, 1333, 924, 26638, 61188, 13360};
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int[] value = {96, 76, 56, 11, 86, 10, 66, 86, 83, 12, 9, 81};
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int[][] weights = {
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{19, 1, 10, 1, 1, 14, 152, 11, 1, 1, 1, 1},
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{0, 4, 53, 0, 0, 80, 0, 4, 5, 0, 0, 0},
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{4, 660, 3, 0, 30, 0, 3, 0, 4, 90, 0, 0},
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{7, 0, 18, 6, 770, 330, 7, 0, 0, 6, 0, 0},
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{0, 20, 0, 4, 52, 3, 0, 0, 0, 5, 4, 0},
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{0, 0, 40, 70, 4, 63, 0, 0, 60, 0, 4, 0},
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{0, 32, 0, 0, 0, 5, 0, 3, 0, 660, 0, 9}
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};
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int maxCapacity = -1;
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for (int c : capacity) {
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if (c > maxCapacity) {
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maxCapacity = c;
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}
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}
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MPVariable[] taken = solver.makeIntVarArray(itemCount, 0, maxCapacity);
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/** constraints */
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MPConstraint constraints[] = new MPConstraint[capacityCount];
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for (int i = 0; i < capacityCount; i++) {
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constraints[i] = solver.makeConstraint(0, capacity[i]);
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for (int j = 0; j < itemCount; j++) {
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constraints[i].setCoefficient(taken[j], weights[i][j]);
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}
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}
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/** objective */
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MPObjective obj = solver.objective();
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obj.setMaximization();
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for (int i = 0; i < itemCount; i++) {
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obj.setCoefficient(taken[i], value[i]);
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}
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solver.solve();
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/** printing */
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System.out.println("Max cost: " + obj.value());
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System.out.print("Item quantities: ");
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for (MPVariable var : taken) {
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System.out.print((int) var.solutionValue() + " ");
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}
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}
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public static void main(String[] args) {
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solve("CBC_MIXED_INTEGER_PROGRAMMING");
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}
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}
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