Backport samples from g3 to gh

ortools/linear_solver/samples/bin_packing_mip.py

@@ -0,0 +1,115 @@
+# 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.
+"""Solve a simple bin packing problem using a MIP solver."""
+# [START program]
+# [START import]
+from __future__ import print_function
+from ortools.linear_solver import pywraplp
+
+# [END import]
+
+
+# [START program_part1]
+# [START data_model]
+def create_data_model():
+    """Create the data for the example."""
+    data = {}
+    weights = [48, 30, 19, 36, 36, 27, 42, 42, 36, 24, 30]
+    data['weights'] = weights
+    data['items'] = list(range(len(weights)))
+    data['bins'] = data['items']
+    data['bin_capacity'] = 100
+    return data
+
+
+# [END data_model]
+
+
+def main():
+    # [START data]
+    data = create_data_model()
+    # [END data]
+    # [END program_part1]
+
+    # [START solver]
+    # Create the mip solver with the CBC backend.
+    solver = pywraplp.Solver('bin_packing_mip',
+                             pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
+    # [END solver]
+
+    # [START program_part2]
+    # [START variables]
+    # Variables
+    # x[i, j] = 1 if item i is packed in bin j.
+    x = {}
+    for i in data['items']:
+        for j in data['bins']:
+            x[(i, j)] = solver.IntVar(0, 1, 'x_%i_%i' % (i, j))
+
+    # y[j] = 1 if bin j is used.
+    y = {}
+    for j in data['bins']:
+        y[j] = solver.IntVar(0, 1, 'y[%i]' % j)
+    # [END variables]
+
+    # [START constraints]
+    # Constraints
+    # Each item must be in exactly one bin.
+    for i in data['items']:
+        solver.Add(sum(x[i, j] for j in data['bins']) == 1)
+
+    # The amount packed in each bin cannot exceed its capacity.
+    for j in data['bins']:
+        solver.Add(
+            sum(x[(i, j)] * data['weights'][i] for i in data['items']) <= y[j] *
+            data['bin_capacity'])
+    # [END constraints]
+
+    # [START objective]
+    # Objective: minimize the number of bins used.
+    solver.Minimize(solver.Sum([y[j] for j in data['bins']]))
+    # [END objective]
+
+    # [START solve]
+    status = solver.Solve()
+    # [END solve]
+
+    # [START print_solution]
+    if status == pywraplp.Solver.OPTIMAL:
+        num_bins = 0.
+        for j in data['bins']:
+            if y[j].solution_value() == 1:
+                bin_items = []
+                bin_weight = 0
+                for i in data['items']:
+                    if x[i, j].solution_value() > 0:
+                        bin_items.append(i)
+                        bin_weight += data['weights'][i]
+                if bin_weight > 0:
+                    num_bins += 1
+                    print('Bin number', j)
+                    print('  Items packed:', bin_items)
+                    print('  Total weight:', bin_weight)
+                    print()
+        print()
+        print('Number of bins used:', num_bins)
+        print('Time = ', solver.WallTime(), ' milliseconds')
+    else:
+        print('The problem does not have an optimal solution.')
+    # [END print_solution]
+
+
+if __name__ == '__main__':
+    main()
+# [END program_part2]
+# [END program]