python

cuOpt Notebooks

Contains a collection of Jupyter Notebooks that outline how cuOpt python API can be used to solve a wide variety of problems

Summary

Each notebook represents an example use case for NVIDIA cuOpt. All notebooks demonstrate high level problem modeling leveraging the cuOpt DataModel and SolverSettings. In addition, each notebook covers additional cuOpt features listed below alongside notebook descriptions

• cost_matrix_creation.ipynb : A notebook demonstrating how to build a cost matrix for various problem types

  • Additional Features:
    • WaypointMatrix
    • Visualization

• cvrp_daily_deliveries.ipynb : A notebook demonstrating a simple delivery use case

  • Additional Features:
    • Min Vehicles Constraint

• cvrptw_service_team_routing.ipynb : A notebook demonstrating service team routing using technicians with varied availability and skillset.

  • Additional Features:
    • Multiple Capacity (and demand) Dimensions
    • Vehicle Time Windows

• cvrpstw_priority_routing.ipynb : A notebook demonstrating routing of mixed priority orders

  • Additional Features:
    • Secondary Cost Matrix
    • Soft Time Windows
    • Penalties

• cpdptw_intra-factory_transport.ipynb : A notebook demonstrating intra-factory routing modeled as a pickup and delivery problem

  • Additional Features:
    • Pickup and Delivery
    • Order Locations
    • Precedence Constraints
    • WaypointMatrix

• cvrptw_benchmark_gehring_homberger.ipynb : A notebook demonstrating a benchmark run using a large academic problem instance.

• pdptw_mixed_fleet.ipynb : A notebook demonstrating heterogenous fleet modeling for a pickup and delivery problem

  • Additional Features:
    • Pickup and Delivery
    • Order Locations