add new code samoke; update internals

ortools/sat/samples/MultipleKnapsackSat.java

@@ -0,0 +1,138 @@
+// Copyright 2010-2018 Google LLC
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// [START program]
+// [START import]
+import com.google.ortools.sat.CpModel;
+import com.google.ortools.sat.CpSolver;
+import com.google.ortools.sat.CpSolverStatus;
+import com.google.ortools.sat.IntVar;
+import com.google.ortools.sat.LinearExpr;
+// [END import]
+
+/** Sample showing how to solve a multiple knapsack problem. */
+public class MultipleKnapsackSat {
+  static {
+    System.loadLibrary("jniortools");
+  }
+
+  // [START data]
+  static class DataModel {
+    int[] items = new int[] {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
+    int[] values = new int[] {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
+    int[] binCapacities = new int[] {100, 100, 100, 100, 100};
+    int numItems = items.length;
+    int numBins = 5;
+  }
+  // [END data]
+
+  // [START solution_printer]
+  static void printSolution(
+      DataModel data, CpSolver solver, IntVar[][] x, IntVar[] load, IntVar[] value) {
+    System.out.printf("Optimal objective value: %f%n", solver.objectiveValue());
+    System.out.println();
+    long packedWeight = 0;
+    long packedValue = 0;
+    for (int b = 0; b < data.numBins; ++b) {
+      System.out.println("Bin " + b);
+      for (int i = 0; i < data.numItems; ++i) {
+        if (solver.value(x[i][b]) > 0) {
+          System.out.println(
+              "Item " + i + "  -  Weight: " + data.items[i] + "  Value: " + data.values[i]);
+        }
+      }
+      System.out.println("Packed bin weight: " + solver.value(load[b]));
+      packedWeight = packedWeight + solver.value(load[b]);
+      System.out.println("Packed bin value: " + solver.value(value[b]) + "\n");
+      packedValue = packedValue + solver.value(value[b]);
+    }
+    System.out.println("Total packed weight: " + packedWeight);
+    System.out.println("Total packed value: " + packedValue);
+  }
+
+  public static void main(String[] args) {
+    // Instantiate the data problem.
+    // [START data]
+    final DataModel data = new DataModel();
+    // [END data]
+    int totalValue = 0;
+    for (int i = 0; i < data.numItems; ++i) {
+      totalValue = totalValue + data.values[i];
+    }
+
+    // [START model]
+    CpModel model = new CpModel();
+    // [END model]
+
+    // [START variables]
+    IntVar[][] x = new IntVar[data.numItems][data.numBins];
+    for (int i = 0; i < data.numItems; ++i) {
+      for (int b = 0; b < data.numBins; ++b) {
+        x[i][b] = model.newIntVar(0, 1, "x_" + i + "_" + b);
+      }
+    }
+    // Main variables.
+    // Load and value variables.
+    IntVar[] load = new IntVar[data.numBins];
+    IntVar[] value = new IntVar[data.numBins];
+    for (int b = 0; b < data.numBins; ++b) {
+      load[b] = model.newIntVar(0, data.binCapacities[b], "load_" + b);
+      value[b] = model.newIntVar(0, totalValue, "value_" + b);
+    }
+
+    // Links load and value with x.
+    int[] sizes = new int[data.numItems];
+    for (int i = 0; i < data.numItems; ++i) {
+      sizes[i] = data.items[i];
+    }
+    for (int b = 0; b < data.numBins; ++b) {
+      IntVar[] vars = new IntVar[data.numItems];
+      for (int i = 0; i < data.numItems; ++i) {
+        vars[i] = x[i][b];
+      }
+      model.addEquality(LinearExpr.scalProd(vars, data.items), load[b]);
+      model.addEquality(LinearExpr.scalProd(vars, data.values), value[b]);
+    }
+    // [END variables]
+
+    // [START constraints]
+    // Each item can be in at most one bin.
+    // Place all items.
+    for (int i = 0; i < data.numItems; ++i) {
+      IntVar[] vars = new IntVar[data.numBins];
+      for (int b = 0; b < data.numBins; ++b) {
+        vars[b] = x[i][b];
+      }
+      model.addLessOrEqual(LinearExpr.sum(vars), 1);
+    }
+    // [END constraints]
+    // Maximize sum of load.
+    // [START objective]
+    model.maximize(LinearExpr.sum(value));
+    // [END objective]
+
+    // [START solve]
+    CpSolver solver = new CpSolver();
+    CpSolverStatus status = solver.solve(model);
+    // [END solve]
+
+    // [START print_solution]
+    System.out.println("Solve status: " + status);
+    if (status == CpSolverStatus.OPTIMAL) {
+      printSolution(data, solver, x, load, value);
+    }
+    // [END print_solution]
+  }
+
+  private MultipleKnapsackSat() {}
+}