Implement other problems as allocation/scheduling problems

examples/binpack/run_cpsat.py

@@ -27,7 +27,7 @@ def run_cpsat():
     p.nb_process = 16
     res = solver.solve(
         parameters_cp=p,
-        time_limit=20,
+        time_limit=100,
         ortools_cpsat_solver_kwargs={"log_search_progress": True},
     )
     sol = res[-1][0]
@@ -64,4 +64,4 @@ def run_cpsat_ws():
 
 
 if __name__ == "__main__":
-    run_cpsat_ws()
+    run_cpsat()

examples/binpack/run_lns_generic.py

@@ -0,0 +1,146 @@
+#  Copyright (c) 2025 AIRBUS and its affiliates.
+#  This source code is licensed under the MIT license found in the
+#  LICENSE file in the root directory of this source tree.
+
+import logging
+
+from discrete_optimization.binpack.parser import (
+    get_data_available_bppc,
+    parse_bin_packing_constraint_file,
+)
+from discrete_optimization.binpack.solvers.cpsat import (
+    CpSatBinPackSolver,
+    ModelingBinPack,
+)
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.constraint_extractor import (
+    BaseConstraintExtractor,
+    ConstraintExtractorList,
+    ConstraintExtractorPortfolio,
+    DummyConstraintExtractor,
+    NbChangesAllocationConstraintExtractor,
+    NbUsagesAllocationConstraintExtractor,
+    SchedulingConstraintExtractor,
+    SubresourcesAllocationConstraintExtractor,
+    SubtasksAllocationConstraintExtractor,
+)
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.constraint_handler import (
+    TasksConstraintHandler,
+)
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.neighbor_tools import (
+    NeighborBuilderMix,
+    NeighborBuilderSubPart,
+    NeighborRandom,
+)
+from discrete_optimization.generic_tools.callbacks.warm_start_callback import (
+    WarmStartCallback,
+)
+from discrete_optimization.generic_tools.cp_tools import ParametersCp
+from discrete_optimization.generic_tools.lns_cp import LnsOrtoolsCpSat
+
+logging.basicConfig(level=logging.INFO)
+
+
+def run_lns():
+    f = [ff for ff in get_data_available_bppc() if "BPPC_6_2_9.txt" in ff][0]
+    problem = parse_bin_packing_constraint_file(f)
+    subsolver = CpSatBinPackSolver(problem=problem)
+    subsolver.init_model(modeling=ModelingBinPack.BINARY, upper_bound=450)
+    print("finished initializing")
+    extractors: list[BaseConstraintExtractor] = [
+        NbChangesAllocationConstraintExtractor(nb_changes_max=20),
+        NbUsagesAllocationConstraintExtractor(plus_delta_nb_usages_total=10),
+        SubresourcesAllocationConstraintExtractor(
+            frac_random_fixed_unary_resources=0.2
+        ),
+        SubtasksAllocationConstraintExtractor(
+            fix_secondary_tasks_allocation=False, frac_random_fixed_tasks=0.2
+        ),
+        DummyConstraintExtractor(),
+    ]
+    constraints_extractor = ConstraintExtractorPortfolio(
+        extractors=extractors, weights=[1 / len(extractors)] * len(extractors)
+    )
+    # constraints_extractor = ConstraintExtractorList(extractors=extractors)
+    constraint_handler = TasksConstraintHandler(
+        problem=problem,
+        constraints_extractor=constraints_extractor,
+    )
+    solver = LnsOrtoolsCpSat(
+        problem=problem,
+        subsolver=subsolver,
+        constraint_handler=constraint_handler,
+    )
+    parameters_cp = ParametersCp.default_cpsat()
+    parameters_cp.nb_process = 15
+    res = solver.solve(
+        callbacks=[WarmStartCallback()],
+        nb_iteration_lns=30,
+        time_limit_subsolver=20,
+        time_limit_subsolver_iter0=20,
+        parameters_cp=parameters_cp,
+        skip_initial_solution_provider=True,
+        ortools_cpsat_solver_kwargs={
+            "log_search_progress": False,
+            "fix_variables_to_their_hinted_value": False,
+        },
+    )
+    sol = res.get_best_solution()
+    problem.satisfy(sol)
+
+
+def run_lns_scheduling():
+    f = [ff for ff in get_data_available_bppc() if "BPPC_4_1_1.txt" in ff][0]
+    problem = parse_bin_packing_constraint_file(f)
+    subsolver = CpSatBinPackSolver(problem=problem)
+    subsolver.init_model(modeling=ModelingBinPack.SCHEDULING, upper_bound=450)
+    print("finished initializing")
+    n = NeighborBuilderMix(
+        list_neighbor=[
+            NeighborBuilderSubPart(problem=problem, nb_cut_part=5),
+            NeighborRandom(problem=problem, fraction_subproblem=0.5),
+        ],
+        weight_neighbor=[1 / 2] * 2,
+        verbose=True,
+    )
+    extractors: list[BaseConstraintExtractor] = [
+        SchedulingConstraintExtractor(
+            plus_delta_primary=100,
+            minus_delta_primary=100,
+            plus_delta_secondary=2,
+            minus_delta_secondary=2,
+        ),
+        DummyConstraintExtractor(),
+    ]
+    constraints_extractor = ConstraintExtractorPortfolio(
+        extractors=extractors, weights=[0.8, 0.2]
+    )
+    constraint_handler = TasksConstraintHandler(
+        problem=problem,
+        neighbor_builder=n,
+        constraints_extractor=constraints_extractor,
+    )
+    solver = LnsOrtoolsCpSat(
+        problem=problem,
+        subsolver=subsolver,
+        constraint_handler=constraint_handler,
+    )
+    parameters_cp = ParametersCp.default_cpsat()
+    parameters_cp.nb_process = 15
+    res = solver.solve(
+        callbacks=[WarmStartCallback()],
+        nb_iteration_lns=30,
+        time_limit_subsolver=10,
+        time_limit_subsolver_iter0=10,
+        parameters_cp=parameters_cp,
+        skip_initial_solution_provider=True,
+        ortools_cpsat_solver_kwargs={
+            "log_search_progress": False,
+            "fix_variables_to_their_hinted_value": False,
+        },
+    )
+    sol = res.get_best_solution()
+    problem.satisfy(sol)
+
+
+if __name__ == "__main__":
+    run_lns_scheduling()

examples/coloring/run_lns_generic.py

@@ -0,0 +1,94 @@
+#  Copyright (c) 2024 AIRBUS and its affiliates.
+#  This source code is licensed under the MIT license found in the
+#  LICENSE file in the root directory of this source tree.
+
+
+import logging
+
+from discrete_optimization.coloring.parser import get_data_available, parse_file
+from discrete_optimization.coloring.plot import plot_coloring_solution, plt
+from discrete_optimization.coloring.problem import (
+    ColoringConstraints,
+    transform_coloring_problem,
+)
+from discrete_optimization.coloring.solvers.cpsat import (
+    CpSatColoringSolver,
+    ModelingCpSat,
+)
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.constraint_extractor import (
+    BaseConstraintExtractor,
+    ConstraintExtractorPortfolio,
+    DummyConstraintExtractor,
+    NbChangesAllocationConstraintExtractor,
+    NbUsagesAllocationConstraintExtractor,
+    SubresourcesAllocationConstraintExtractor,
+    SubtasksAllocationConstraintExtractor,
+)
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.constraint_handler import (
+    TasksConstraintHandler,
+)
+from discrete_optimization.generic_tools.callbacks.loggers import NbIterationTracker
+from discrete_optimization.generic_tools.callbacks.warm_start_callback import (
+    WarmStartCallback,
+)
+from discrete_optimization.generic_tools.cp_tools import ParametersCp
+from discrete_optimization.generic_tools.lns_cp import LnsOrtoolsCpSat
+
+logging.basicConfig(level=logging.INFO)
+
+
+def run_lns():
+    logging.basicConfig(level=logging.INFO)
+    file = [f for f in get_data_available() if "gc_250_5" in f][0]
+    color_problem = parse_file(file)
+    subsolver = CpSatColoringSolver(color_problem, params_objective_function=None)
+    subsolver.init_model(
+        modeling=ModelingCpSat.BINARY,
+        do_warmstart=True,
+        value_sequence_chain=False,
+        used_variable=True,
+    )
+    p = ParametersCp.default_cpsat()
+    print("finished initializing")
+    extractors: list[BaseConstraintExtractor] = [
+        NbChangesAllocationConstraintExtractor(nb_changes_max=20),
+        NbUsagesAllocationConstraintExtractor(plus_delta_nb_usages_total=10),
+        # SubresourcesAllocationConstraintExtractor(frac_random_fixed_unary_resources=0.2),
+        SubtasksAllocationConstraintExtractor(
+            fix_secondary_tasks_allocation=False, frac_random_fixed_tasks=0.2
+        ),
+        DummyConstraintExtractor(),
+    ]
+    constraints_extractor = ConstraintExtractorPortfolio(
+        extractors=extractors, weights=[1 / len(extractors)] * len(extractors)
+    )
+    # constraints_extractor = ConstraintExtractorList(extractors=extractors)
+    constraint_handler = TasksConstraintHandler(
+        problem=color_problem,
+        constraints_extractor=constraints_extractor,
+    )
+    solver = LnsOrtoolsCpSat(
+        problem=color_problem,
+        subsolver=subsolver,
+        constraint_handler=constraint_handler,
+    )
+    parameters_cp = ParametersCp.default_cpsat()
+    parameters_cp.nb_process = 15
+    res = solver.solve(
+        callbacks=[WarmStartCallback()],
+        nb_iteration_lns=30,
+        time_limit_subsolver=20,
+        time_limit_subsolver_iter0=20,
+        parameters_cp=parameters_cp,
+        skip_initial_solution_provider=True,
+        ortools_cpsat_solver_kwargs={
+            "log_search_progress": False,
+            "fix_variables_to_their_hinted_value": False,
+        },
+    )
+    sol = res.get_best_solution()
+    print(color_problem.satisfy(sol))
+
+
+if __name__ == "__main__":
+    run_lns()

examples/facility/run_cpsat.py

@@ -0,0 +1,35 @@
+#  Copyright (c) 2025 AIRBUS and its affiliates.
+#  This source code is licensed under the MIT license found in the
+#  LICENSE file in the root directory of this source tree.
+import logging
+import os.path
+
+from discrete_optimization.facility.parser import get_data_available, parse_file
+from discrete_optimization.facility.problem import FacilityProblem, FacilitySolution
+from discrete_optimization.facility.solvers.cpsat import CpSatFacilitySolver
+from discrete_optimization.generic_tools.cp_tools import ParametersCp
+
+logging.basicConfig(level=logging.INFO)
+
+
+def cp_facility_example():
+    file = [f for f in get_data_available() if "fl_200_7" in f][0]
+    problem: FacilityProblem = parse_file(file)
+    print("customer : ", problem.customer_count, "facility : ", problem.facility_count)
+    solver = CpSatFacilitySolver(problem=problem)
+    print("Initializing...")
+    solver.init_model()
+    print("finished initializing")
+    res = solver.solve(
+        parameters_cp=ParametersCp.default_cpsat(),
+        time_limit=100,
+        ortools_cpsat_solver_kwargs={"log_search_progress": True},
+    )
+    sol, fit = res.get_best_solution_fit()
+    print(problem.evaluate(sol))
+    print(problem.satisfy(sol))
+    print(fit)
+
+
+if __name__ == "__main__":
+    cp_facility_example()

examples/facility/run_lns_generic.py

@@ -0,0 +1,85 @@
+#  Copyright (c) 2025 AIRBUS and its affiliates.
+#  This source code is licensed under the MIT license found in the
+#  LICENSE file in the root directory of this source tree.
+import logging
+import os.path
+
+from discrete_optimization.facility.parser import get_data_available, parse_file
+from discrete_optimization.facility.problem import FacilityProblem, FacilitySolution
+from discrete_optimization.facility.solvers.cpsat import CpSatFacilitySolver
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.constraint_extractor import (
+    BaseConstraintExtractor,
+    ConstraintExtractorList,
+    ConstraintExtractorPortfolio,
+    DummyConstraintExtractor,
+    NbChangesAllocationConstraintExtractor,
+    NbUsagesAllocationConstraintExtractor,
+    SubresourcesAllocationConstraintExtractor,
+    SubtasksAllocationConstraintExtractor,
+)
+from discrete_optimization.generic_tasks_tools.solvers.lns_cp.constraint_handler import (
+    TasksConstraintHandler,
+)
+from discrete_optimization.generic_tools.callbacks.warm_start_callback import (
+    WarmStartCallback,
+    WarmStartCallbackLastRun,
+)
+from discrete_optimization.generic_tools.cp_tools import ParametersCp
+from discrete_optimization.generic_tools.lns_cp import LnsOrtoolsCpSat
+
+logging.basicConfig(level=logging.INFO)
+
+
+def run_lns_cp_facility():
+    file = [f for f in get_data_available() if "fl_200_6" in f][0]
+    problem: FacilityProblem = parse_file(file)
+    print("customer : ", problem.customer_count, "facility : ", problem.facility_count)
+    subsolver = CpSatFacilitySolver(problem=problem)
+    print("Initializing...")
+    subsolver.init_model()
+    print("finished initializing")
+    extractors: list[BaseConstraintExtractor] = [
+        # NbChangesAllocationConstraintExtractor(nb_changes_max=20),
+        NbUsagesAllocationConstraintExtractor(plus_delta_nb_usages_total=5),
+        SubresourcesAllocationConstraintExtractor(
+            frac_random_fixed_unary_resources=0.2
+        ),
+        SubtasksAllocationConstraintExtractor(
+            fix_secondary_tasks_allocation=False, frac_random_fixed_tasks=0.2
+        ),
+        DummyConstraintExtractor(),
+    ]
+    constraints_extractor = ConstraintExtractorPortfolio(
+        extractors=extractors, weights=[1 / len(extractors)] * len(extractors)
+    )
+
+    constraint_handler = TasksConstraintHandler(
+        problem=problem,
+        constraints_extractor=constraints_extractor,
+    )
+
+    solver = LnsOrtoolsCpSat(
+        problem=problem,
+        subsolver=subsolver,
+        constraint_handler=constraint_handler,
+    )
+    parameters_cp = ParametersCp.default_cpsat()
+    parameters_cp.nb_process = 15
+    res = solver.solve(
+        callbacks=[WarmStartCallback()],
+        nb_iteration_lns=30,
+        time_limit_subsolver=10,
+        time_limit_subsolver_iter0=20,
+        parameters_cp=parameters_cp,
+        skip_initial_solution_provider=True,
+        ortools_cpsat_solver_kwargs={
+            "log_search_progress": False,
+            "fix_variables_to_their_hinted_value": False,
+        },
+    )
+    sol = res.get_best_solution()
+    problem.satisfy(sol)
+
+
+if __name__ == "__main__":
+    run_lns_cp_facility()