add no overlap sample
This commit is contained in:
Laurent Perron
@@ -253,6 +253,264 @@ public class CodeSamplesSat
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## NoOverlap constraint
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A no overlap constraint simply states that all intervals are disjoint.
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It is build with a list of interval variables. Fixed intervals are useful to
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exclude part of the timeline.
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### Python code
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```python
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"""Code sample to demonstrates how to build a no overlap constraint."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from ortools.sat.python import cp_model
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def NoOverlapSample():
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"""No overlap sample with fixed activities."""
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model = cp_model.CpModel()
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horizon = 21 # 3 weeks.
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# Task 0, duration 2.
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start_0 = model.NewIntVar(0, horizon, 'start_0')
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duration_0 = 2 # Python cp/sat code accept integer variables or constants.
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end_0 = model.NewIntVar(0, horizon, 'end_0')
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task_0 = model.NewIntervalVar(start_0, duration_0, end_0, 'task_0')
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# Task 1, duration 4.
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start_1 = model.NewIntVar(0, horizon, 'start_1')
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duration_1 = 4 # Python cp/sat code accept integer variables or constants.
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end_1 = model.NewIntVar(0, horizon, 'end_1')
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task_1 = model.NewIntervalVar(start_1, duration_1, end_1, 'task_1')
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# Task 2, duration 3.
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start_2 = model.NewIntVar(0, horizon, 'start_2')
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duration_2 = 3 # Python cp/sat code accept integer variables or constants.
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end_2 = model.NewIntVar(0, horizon, 'end_2')
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task_2 = model.NewIntervalVar(start_2, duration_2, end_2, 'task_2')
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# Week ends.
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weekend_0 = model.NewIntervalVar(5, 2, 7, 'weekend_0')
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weekend_1 = model.NewIntervalVar(12, 2, 14, 'weekend_1')
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weekend_2 = model.NewIntervalVar(19, 2, 21, 'weekend_2')
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# No Overlap constraint.
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model.AddNoOverlap([task_0, task_1, task_2, weekend_0, weekend_1, weekend_2])
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# Makespan objective.
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obj = model.NewIntVar(0, horizon, 'makespan')
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model.AddMaxEquality(obj, [end_0, end_1, end_2])
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model.Minimize(obj)
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# Solve model.
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solver = cp_model.CpSolver()
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status = solver.Solve(model)
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if status == cp_model.OPTIMAL:
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# Print out makespan and the starts of all tasks.
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print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
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print('Task 0 starts at %i' % solver.Value(start_0))
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print('Task 1 starts at %i' % solver.Value(start_1))
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print('Task 2 starts at %i' % solver.Value(start_2))
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NoOverlapSample()
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```
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### C++ code
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```cpp
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#include "ortools/sat/cp_model.pb.h"
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#include "ortools/sat/cp_model_solver.h"
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#include "ortools/sat/cp_model_utils.h"
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#include "ortools/sat/model.h"
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namespace operations_research {
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namespace sat {
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void NoOverlapSample() {
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CpModelProto cp_model;
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const int kHorizon = 21; // 3 weeks.
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auto new_variable = [&cp_model](int64 lb, int64 ub) {
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CHECK_LE(lb, ub);
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const int index = cp_model.variables_size();
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IntegerVariableProto* const var = cp_model.add_variables();
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var->add_domain(lb);
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var->add_domain(ub);
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return index;
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};
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auto new_constant = [&new_variable](int64 v) { return new_variable(v, v); };
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auto new_interval = [&cp_model](int start, int duration, int end) {
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const int index = cp_model.constraints_size();
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IntervalConstraintProto* const interval =
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cp_model.add_constraints()->mutable_interval();
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interval->set_start(start);
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interval->set_size(duration);
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interval->set_end(end);
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return index;
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};
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auto new_fixed_interval = [&cp_model, &new_constant](int64 start,
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int64 duration) {
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const int index = cp_model.constraints_size();
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IntervalConstraintProto* const interval =
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cp_model.add_constraints()->mutable_interval();
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interval->set_start(new_constant(start));
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interval->set_size(new_constant(duration));
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interval->set_end(new_constant(start + duration));
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return index;
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};
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auto add_no_overlap = [&cp_model](const std::vector<int>& intervals) {
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NoOverlapConstraintProto* const no_overlap =
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cp_model.add_constraints()->mutable_no_overlap();
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for (const int i : intervals) {
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no_overlap->add_intervals(i);
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}
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};
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auto add_precedence = [&cp_model](int before, int after) {
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LinearConstraintProto* const lin =
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cp_model.add_constraints()->mutable_linear();
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lin->add_vars(after);
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lin->add_coeffs(1L);
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lin->add_vars(before);
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lin->add_coeffs(-1L);
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lin->add_domain(0);
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lin->add_domain(kint64max);
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};
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// Task 0, duration 2.
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const int start_0 = new_variable(0, kHorizon);
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const int duration_0 = new_constant(2);
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const int end_0 = new_variable(0, kHorizon);
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const int task_0 = new_interval(start_0, duration_0, end_0);
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// Task 1, duration 4.
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const int start_1 = new_variable(0, kHorizon);
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const int duration_1 = new_constant(4);
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const int end_1 = new_variable(0, kHorizon);
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const int task_1 = new_interval(start_1, duration_1, end_1);
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// Task 2, duration 3.
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const int start_2 = new_variable(0, kHorizon);
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const int duration_2 = new_constant(3);
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const int end_2 = new_variable(0, kHorizon);
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const int task_2 = new_interval(start_2, duration_2, end_2);
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// Week ends.
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const int weekend_0 = new_fixed_interval(5, 2);
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const int weekend_1 = new_fixed_interval(12, 2);
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const int weekend_2 = new_fixed_interval(19, 2);
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// No Overlap constraint.
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add_no_overlap({task_0, task_1, task_2, weekend_0, weekend_1, weekend_2});
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// Makespan.
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const int makespan = new_variable(0, kHorizon);
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add_precedence(end_0, makespan);
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add_precedence(end_1, makespan);
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add_precedence(end_2, makespan);
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CpObjectiveProto* const obj = cp_model.mutable_objective();
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obj->add_vars(makespan);
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obj->add_coeffs(1); // Minimization.
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// Solving part.
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Model model;
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LOG(INFO) << CpModelStats(cp_model);
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const CpSolverResponse response = SolveCpModel(cp_model, &model);
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LOG(INFO) << CpSolverResponseStats(response);
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if (response.status() == CpSolverStatus::OPTIMAL) {
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LOG(INFO) << "Optimal Schedule Length: " << response.objective_value();
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LOG(INFO) << "Task 0 starts at " << response.solution(start_0);
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LOG(INFO) << "Task 1 starts at " << response.solution(start_1);
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LOG(INFO) << "Task 2 starts at " << response.solution(start_2);
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}
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}
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} // namespace sat
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} // namespace operations_research
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int main() {
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operations_research::sat::NoOverlapSample();
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return EXIT_SUCCESS;
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}
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```
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### C\# code
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```cs
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using System;
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using Google.OrTools.Sat;
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public class CodeSamplesSat
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{
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static void NoOverlapSample()
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{
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CpModel model = new CpModel();
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int horizon = 21;
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// Task 0, duration 2.
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IntVar start_0 = model.NewIntVar(0, horizon, "start_0");
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int duration_0 = 2;
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IntVar end_0 = model.NewIntVar(0, horizon, "end_0");
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IntervalVar task_0 =
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model.NewIntervalVar(start_0, duration_0, end_0, "task_0");
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// Task 1, duration 4.
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IntVar start_1 = model.NewIntVar(0, horizon, "start_1");
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int duration_1 = 4;
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IntVar end_1 = model.NewIntVar(0, horizon, "end_1");
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IntervalVar task_1 =
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model.NewIntervalVar(start_1, duration_1, end_1, "task_1");
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// Task 2, duration 3.
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IntVar start_2 = model.NewIntVar(0, horizon, "start_2");
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int duration_2 = 3;
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IntVar end_2 = model.NewIntVar(0, horizon, "end_2");
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IntervalVar task_2 =
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model.NewIntervalVar(start_2, duration_2, end_2, "task_2");
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// Week ends.
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IntervalVar weekend_0 = model.NewIntervalVar(5, 2, 7, "weekend_0");
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IntervalVar weekend_1 = model.NewIntervalVar(12, 2, 14, "weekend_1");
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IntervalVar weekend_2 = model.NewIntervalVar(19, 2, 21, "weekend_2");
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// No Overlap constraint.
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model.AddNoOverlap(new IntervalVar[] {task_0, task_1, task_2, weekend_0,
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weekend_1, weekend_2});
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// Makespan objective.
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IntVar obj = model.NewIntVar(0, horizon, "makespan");
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model.AddMaxEquality(obj, new IntVar[] {end_0, end_1, end_2});
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model.Minimize(obj);
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// Creates a solver and solves the model.
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CpSolver solver = new CpSolver();
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CpSolverStatus status = solver.Solve(model);
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if (status == CpSolverStatus.Optimal)
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{
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Console.WriteLine("Optimal Schedule Length: " + solver.ObjectiveValue);
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Console.WriteLine("Task 0 starts at " + solver.Value(start_0));
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Console.WriteLine("Task 1 starts at " + solver.Value(start_1));
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Console.WriteLine("Task 2 starts at " + solver.Value(start_2));
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}
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}
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static void Main()
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{
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NoOverlapSample();
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}
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}
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```
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## Cumulative constraint
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@@ -262,6 +520,8 @@ at that time point is less than a given capacity.
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## Alternative resources for one interval
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## Ranking or tasks in a disjunctive resource
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## Transitions in a disjunctive resource
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## Precedences between intervals
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134
ortools/sat/samples/no_overlap_sample.cc
Normal file
134
ortools/sat/samples/no_overlap_sample.cc
Normal file
@@ -0,0 +1,134 @@
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// Copyright 2010-2017 Google
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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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#include "ortools/sat/cp_model.pb.h"
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#include "ortools/sat/cp_model_solver.h"
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#include "ortools/sat/cp_model_utils.h"
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#include "ortools/sat/model.h"
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namespace operations_research {
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namespace sat {
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void NoOverlapSample() {
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CpModelProto cp_model;
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const int kHorizon = 21; // 3 weeks.
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auto new_variable = [&cp_model](int64 lb, int64 ub) {
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CHECK_LE(lb, ub);
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const int index = cp_model.variables_size();
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IntegerVariableProto* const var = cp_model.add_variables();
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var->add_domain(lb);
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var->add_domain(ub);
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return index;
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};
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auto new_constant = [&new_variable](int64 v) { return new_variable(v, v); };
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auto new_interval = [&cp_model](int start, int duration, int end) {
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const int index = cp_model.constraints_size();
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IntervalConstraintProto* const interval =
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cp_model.add_constraints()->mutable_interval();
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interval->set_start(start);
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interval->set_size(duration);
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interval->set_end(end);
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return index;
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};
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auto new_fixed_interval = [&cp_model, &new_constant](int64 start,
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int64 duration) {
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const int index = cp_model.constraints_size();
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IntervalConstraintProto* const interval =
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cp_model.add_constraints()->mutable_interval();
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interval->set_start(new_constant(start));
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interval->set_size(new_constant(duration));
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interval->set_end(new_constant(start + duration));
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return index;
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};
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auto add_no_overlap = [&cp_model](const std::vector<int>& intervals) {
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NoOverlapConstraintProto* const no_overlap =
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cp_model.add_constraints()->mutable_no_overlap();
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for (const int i : intervals) {
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no_overlap->add_intervals(i);
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}
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};
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auto add_precedence = [&cp_model](int before, int after) {
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LinearConstraintProto* const lin =
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cp_model.add_constraints()->mutable_linear();
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lin->add_vars(after);
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lin->add_coeffs(1L);
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lin->add_vars(before);
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lin->add_coeffs(-1L);
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lin->add_domain(0);
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lin->add_domain(kint64max);
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};
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// Task 0, duration 2.
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const int start_0 = new_variable(0, kHorizon);
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const int duration_0 = new_constant(2);
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const int end_0 = new_variable(0, kHorizon);
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const int task_0 = new_interval(start_0, duration_0, end_0);
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// Task 1, duration 4.
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const int start_1 = new_variable(0, kHorizon);
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const int duration_1 = new_constant(4);
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const int end_1 = new_variable(0, kHorizon);
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const int task_1 = new_interval(start_1, duration_1, end_1);
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// Task 2, duration 3.
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const int start_2 = new_variable(0, kHorizon);
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const int duration_2 = new_constant(3);
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const int end_2 = new_variable(0, kHorizon);
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const int task_2 = new_interval(start_2, duration_2, end_2);
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// Week ends.
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const int weekend_0 = new_fixed_interval(5, 2);
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const int weekend_1 = new_fixed_interval(12, 2);
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const int weekend_2 = new_fixed_interval(19, 2);
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// No Overlap constraint.
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add_no_overlap({task_0, task_1, task_2, weekend_0, weekend_1, weekend_2});
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// Makespan.
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const int makespan = new_variable(0, kHorizon);
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add_precedence(end_0, makespan);
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add_precedence(end_1, makespan);
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add_precedence(end_2, makespan);
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CpObjectiveProto* const obj = cp_model.mutable_objective();
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obj->add_vars(makespan);
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obj->add_coeffs(1); // Minimization.
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// Solving part.
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Model model;
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LOG(INFO) << CpModelStats(cp_model);
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const CpSolverResponse response = SolveCpModel(cp_model, &model);
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LOG(INFO) << CpSolverResponseStats(response);
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if (response.status() == CpSolverStatus::OPTIMAL) {
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LOG(INFO) << "Optimal Schedule Length: " << response.objective_value();
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LOG(INFO) << "Task 0 starts at " << response.solution(start_0);
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LOG(INFO) << "Task 1 starts at " << response.solution(start_1);
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LOG(INFO) << "Task 2 starts at " << response.solution(start_2);
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}
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}
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} // namespace sat
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} // namespace operations_research
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int main() {
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operations_research::sat::NoOverlapSample();
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return EXIT_SUCCESS;
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}
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76
ortools/sat/samples/no_overlap_sample.cs
Normal file
76
ortools/sat/samples/no_overlap_sample.cs
Normal file
@@ -0,0 +1,76 @@
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// Copyright 2010-2017 Google
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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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using System;
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using Google.OrTools.Sat;
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public class CodeSamplesSat
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{
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static void NoOverlapSample()
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{
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CpModel model = new CpModel();
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int horizon = 21;
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// Task 0, duration 2.
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IntVar start_0 = model.NewIntVar(0, horizon, "start_0");
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int duration_0 = 2;
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IntVar end_0 = model.NewIntVar(0, horizon, "end_0");
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IntervalVar task_0 =
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model.NewIntervalVar(start_0, duration_0, end_0, "task_0");
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// Task 1, duration 4.
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IntVar start_1 = model.NewIntVar(0, horizon, "start_1");
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int duration_1 = 4;
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IntVar end_1 = model.NewIntVar(0, horizon, "end_1");
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IntervalVar task_1 =
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model.NewIntervalVar(start_1, duration_1, end_1, "task_1");
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// Task 2, duration 3.
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IntVar start_2 = model.NewIntVar(0, horizon, "start_2");
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int duration_2 = 3;
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IntVar end_2 = model.NewIntVar(0, horizon, "end_2");
|
||||
IntervalVar task_2 =
|
||||
model.NewIntervalVar(start_2, duration_2, end_2, "task_2");
|
||||
|
||||
// Week ends.
|
||||
IntervalVar weekend_0 = model.NewIntervalVar(5, 2, 7, "weekend_0");
|
||||
IntervalVar weekend_1 = model.NewIntervalVar(12, 2, 14, "weekend_1");
|
||||
IntervalVar weekend_2 = model.NewIntervalVar(19, 2, 21, "weekend_2");
|
||||
|
||||
// No Overlap constraint.
|
||||
model.AddNoOverlap(new IntervalVar[] {task_0, task_1, task_2, weekend_0,
|
||||
weekend_1, weekend_2});
|
||||
|
||||
// Makespan objective.
|
||||
IntVar obj = model.NewIntVar(0, horizon, "makespan");
|
||||
model.AddMaxEquality(obj, new IntVar[] {end_0, end_1, end_2});
|
||||
model.Minimize(obj);
|
||||
|
||||
// Creates a solver and solves the model.
|
||||
CpSolver solver = new CpSolver();
|
||||
CpSolverStatus status = solver.Solve(model);
|
||||
|
||||
if (status == CpSolverStatus.Optimal)
|
||||
{
|
||||
Console.WriteLine("Optimal Schedule Length: " + solver.ObjectiveValue);
|
||||
Console.WriteLine("Task 0 starts at " + solver.Value(start_0));
|
||||
Console.WriteLine("Task 1 starts at " + solver.Value(start_1));
|
||||
Console.WriteLine("Task 2 starts at " + solver.Value(start_2));
|
||||
}
|
||||
}
|
||||
|
||||
static void Main()
|
||||
{
|
||||
NoOverlapSample();
|
||||
}
|
||||
}
|
||||
69
ortools/sat/samples/no_overlap_sample.py
Normal file
69
ortools/sat/samples/no_overlap_sample.py
Normal file
@@ -0,0 +1,69 @@
|
||||
# Copyright 2010-2017 Google
|
||||
# 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.
|
||||
"""Code sample to demonstrates how to build a no overlap constraint."""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from ortools.sat.python import cp_model
|
||||
|
||||
|
||||
def NoOverlapSample():
|
||||
"""No overlap sample with fixed activities."""
|
||||
model = cp_model.CpModel()
|
||||
horizon = 21 # 3 weeks.
|
||||
|
||||
# Task 0, duration 2.
|
||||
start_0 = model.NewIntVar(0, horizon, 'start_0')
|
||||
duration_0 = 2 # Python cp/sat code accept integer variables or constants.
|
||||
end_0 = model.NewIntVar(0, horizon, 'end_0')
|
||||
task_0 = model.NewIntervalVar(start_0, duration_0, end_0, 'task_0')
|
||||
# Task 1, duration 4.
|
||||
start_1 = model.NewIntVar(0, horizon, 'start_1')
|
||||
duration_1 = 4 # Python cp/sat code accept integer variables or constants.
|
||||
end_1 = model.NewIntVar(0, horizon, 'end_1')
|
||||
task_1 = model.NewIntervalVar(start_1, duration_1, end_1, 'task_1')
|
||||
|
||||
# Task 2, duration 3.
|
||||
start_2 = model.NewIntVar(0, horizon, 'start_2')
|
||||
duration_2 = 3 # Python cp/sat code accept integer variables or constants.
|
||||
end_2 = model.NewIntVar(0, horizon, 'end_2')
|
||||
task_2 = model.NewIntervalVar(start_2, duration_2, end_2, 'task_2')
|
||||
|
||||
# Week ends.
|
||||
weekend_0 = model.NewIntervalVar(5, 2, 7, 'weekend_0')
|
||||
weekend_1 = model.NewIntervalVar(12, 2, 14, 'weekend_1')
|
||||
weekend_2 = model.NewIntervalVar(19, 2, 21, 'weekend_2')
|
||||
|
||||
# No Overlap constraint.
|
||||
model.AddNoOverlap([task_0, task_1, task_2, weekend_0, weekend_1, weekend_2])
|
||||
|
||||
# Makespan objective.
|
||||
obj = model.NewIntVar(0, horizon, 'makespan')
|
||||
model.AddMaxEquality(obj, [end_0, end_1, end_2])
|
||||
model.Minimize(obj)
|
||||
|
||||
# Solve model.
|
||||
solver = cp_model.CpSolver()
|
||||
status = solver.Solve(model)
|
||||
|
||||
if status == cp_model.OPTIMAL:
|
||||
# Print out makespan and the starts of all tasks.
|
||||
print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
|
||||
print('Task 0 starts at %i' % solver.Value(start_0))
|
||||
print('Task 1 starts at %i' % solver.Value(start_1))
|
||||
print('Task 2 starts at %i' % solver.Value(start_2))
|
||||
|
||||
|
||||
NoOverlapSample()
|
||||
Reference in New Issue
Block a user