4f381f6d07
* bump abseil to 20250814 * bump protobuf to v32.0 * cmake: add ccache auto support * backport flatzinc, math_opt and sat update
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2025 Google LLC
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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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# [START program]
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"""Code sample to demonstrate how to build a NoOverlap constraint."""
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from ortools.sat.python import cp_model
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def no_overlap_sample_sat():
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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.new_int_var(0, horizon, "start_0")
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duration_0 = 2 # Python cp/sat code accepts integer variables or constants.
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end_0 = model.new_int_var(0, horizon, "end_0")
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task_0 = model.new_interval_var(start_0, duration_0, end_0, "task_0")
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# Task 1, duration 4.
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start_1 = model.new_int_var(0, horizon, "start_1")
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duration_1 = 4 # Python cp/sat code accepts integer variables or constants.
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end_1 = model.new_int_var(0, horizon, "end_1")
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task_1 = model.new_interval_var(start_1, duration_1, end_1, "task_1")
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# Task 2, duration 3.
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start_2 = model.new_int_var(0, horizon, "start_2")
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duration_2 = 3 # Python cp/sat code accepts integer variables or constants.
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end_2 = model.new_int_var(0, horizon, "end_2")
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task_2 = model.new_interval_var(start_2, duration_2, end_2, "task_2")
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# Weekends.
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weekend_0 = model.new_interval_var(5, 2, 7, "weekend_0")
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weekend_1 = model.new_interval_var(12, 2, 14, "weekend_1")
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weekend_2 = model.new_interval_var(19, 2, 21, "weekend_2")
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# No Overlap constraint.
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model.add_no_overlap([task_0, task_1, task_2, weekend_0, weekend_1, weekend_2])
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# Makespan objective.
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obj = model.new_int_var(0, horizon, "makespan")
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model.add_max_equality(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 start times for all tasks.
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print(f"Optimal Schedule Length: {solver.objective_value}")
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print(f"Task 0 starts at {solver.value(start_0)}")
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print(f"Task 1 starts at {solver.value(start_1)}")
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print(f"Task 2 starts at {solver.value(start_2)}")
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else:
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print(f"Solver exited with nonoptimal status: {status}")
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no_overlap_sample_sat()
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# [END program]
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