The following examples showcase how to use the different Operations Research libraries.
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Constraint Solver examples:
- cryptarithm.cc Demonstrates the use of basic modeling objects (integer variables, arithmetic constraints and expressions, simple search).
- golomb.cc Demonstrates how to handle objective functions and collect solutions found during the search.
- magic_square.cc Shows how to use the automatic search to solve your problem.
- costas_array.cc Solves the problem of Costas Array (a constrained assignment problem used for radars) with two version. On version is a feasibility version with hard constraints, the other version is an optimization version with soft constraints and violation costs.
- jobshop.cc Demonstrates scheduling of jobs on different machines.
- jobshop_ls.cc Demonstrates scheduling of jobs on different machines with a search using Local Search and Large Neighorhood Search.
- nqueens.cc Solves the n-queen problem. It also demonstrates how to break symmetries during search.
- network_routing.cc Solves a multicommodity mono-routing problem with capacity constraints and a max usage cost structure.
- sports_scheduling.cc Finds a soccer championship schedule. Its uses an original approach where all constraints attached to either one team, or one week are regrouped into one global 'AllowedAssignment' constraints.
- dobble_ls.cc Shows how to write Local Search operators and Local Search filters in a context of an assignment/partitioning problem. It also shows how to write a simple constraint.
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Routing examples:
- tsp.cc Travelling Salesman Problem.
- cvrptw.cc Capacitated Vehicle Routing Problem with Time Windows.
- pdptw.cc Pickup and Delivery Problem with Time Windows.
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Graph examples:
- flow_api.cc Demonstrates how to use Min-Cost Flow and Max-Flow api.
- linear_assignment_api.cc Demonstrates how to use the Linear Sum Assignment solver.
- dimacs_assignment.cc Solves DIMACS challenge on assignment problems.
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Linear and integer programming examples:
- linear_programming.cc Demonstrates how to use the linear solver wrapper API to solve Linear Programming problems.
- integer_programming.cc Demonstrates how to use the linear solver wrapper API to solve Integer Programming problems.
- linear_solver_protocol_buffers.cc Demonstrates how protocol buffers can be used as input and output to the linear solver wrapper.
- strawberry_fields_with_column_generation.cc Complex example that demonstrates how to use dynamic column generation to solve a 2D covering problem.
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Utilities
- model_util.cc A utility to manipulate model files (.cp) dumped by the solver.
Running the examples will involve building them, then running them.
You can run the following command from the top directory:
make build SOURCE=examples/cpp/<example>.cc
make run SOURCE=examples/cpp/<example>.cc