forked from google/or-tools
-
Notifications
You must be signed in to change notification settings - Fork 3
/
binpacking_problem_sat.cc
92 lines (76 loc) · 2.61 KB
/
binpacking_problem_sat.cc
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
// 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.
#include "ortools/sat/cp_model.h"
namespace operations_research {
namespace sat {
void BinpackingProblemSat() {
// Data.
const int kBinCapacity = 100;
const int kSlackCapacity = 20;
const int kNumBins = 5;
const std::vector<std::vector<int>> items = {
{20, 6}, {15, 6}, {30, 4}, {45, 3}};
const int num_items = items.size();
// Model.
CpModelBuilder cp_model;
// Main variables.
std::vector<std::vector<IntVar>> x(num_items);
for (int i = 0; i < num_items; ++i) {
const int num_copies = items[i][1];
for (int b = 0; b < kNumBins; ++b) {
x[i].push_back(cp_model.NewIntVar({0, num_copies}));
}
}
// Load variables.
std::vector<IntVar> load(kNumBins);
for (int b = 0; b < kNumBins; ++b) {
load[b] = cp_model.NewIntVar({0, kBinCapacity});
}
// Slack variables.
std::vector<BoolVar> slacks(kNumBins);
for (int b = 0; b < kNumBins; ++b) {
slacks[b] = cp_model.NewBoolVar();
}
// Links load and x.
for (int b = 0; b < kNumBins; ++b) {
LinearExpr expr;
for (int i = 0; i < num_items; ++i) {
expr.AddTerm(x[i][b], items[i][0]);
}
cp_model.AddEquality(expr, load[b]);
}
// Place all items.
for (int i = 0; i < num_items; ++i) {
cp_model.AddEquality(LinearExpr::Sum(x[i]), items[i][1]);
}
// Links load and slack through an equivalence relation.
const int safe_capacity = kBinCapacity - kSlackCapacity;
for (int b = 0; b < kNumBins; ++b) {
// slack[b] => load[b] <= safe_capacity.
cp_model.AddLessOrEqual(load[b], safe_capacity).OnlyEnforceIf(slacks[b]);
// not(slack[b]) => load[b] > safe_capacity.
cp_model.AddGreaterThan(load[b], safe_capacity)
.OnlyEnforceIf(Not(slacks[b]));
}
// Maximize sum of slacks.
cp_model.Maximize(LinearExpr::BooleanSum(slacks));
// Solving part.
const CpSolverResponse response = Solve(cp_model.Build());
LOG(INFO) << CpSolverResponseStats(response);
}
} // namespace sat
} // namespace operations_research
int main() {
operations_research::sat::BinpackingProblemSat();
return EXIT_SUCCESS;
}