ortools-routing-modeling

OR-Tools Routing Modeling

Use this skill for vehicle-routing models built with ortools.constraint_solver.pywrapcp.RoutingModel.

Modeling Flow

1. Normalize integer data and map external nodes to routing-manager nodes.
2. Build transit, cost, and demand matrices or vectors before registering callbacks.
3. Add time and capacity dimensions, including vehicle start/end ranges and span limits.
4. Add problem-specific visits, time windows, optional-node penalties, and pairing constraints.
5. Solve with an internal time limit, extract ordered routes, then audit them independently before reporting.

Data And Indexing

• Convert travel times, service times, demands, and time bounds to integers before registering callbacks or transit matrices. Avoid floats and NaN; OR-Tools can silently treat bad values as usable arcs.
• Assert that matrix dimensions, node counts, vehicle counts, and declared record counts agree before building the model. Shape mistakes often produce plausible but meaningless routes.
• Keep a clear distinction between external node IDs, routing manager node IDs, and internal routing indices. Convert with manager.NodeToIndex(node) and manager.IndexToNode(index).
• When vehicles need distinct starts or ends but the input has shared depots, copy the depot into one internal start node and one internal end node per vehicle. Prefer these internal depot copies over reusing the same shared depot node for every vehicle, then map the copies back to the shared external depot IDs when reporting.
• A common cloned-depot layout is: solver job nodes first, then one start depot per vehicle, then one end depot per vehicle. External shared depot IDs can be stripped from the job matrix and restored only when reporting.
• Treat negative, missing, or sentinel travel times as invalid arcs. Either forbid them with explicit constraints or give them a prohibitive transit cost and reject any final route that uses them. Choose invalid-arc sentinels large enough to dominate feasible legs, but small enough to avoid integer overflow when service times and route spans are added.
• For static integer data, prefer precomputed RegisterTransitMatrix and RegisterUnaryTransitVector calls over Python callbacks. Routing callbacks are called very often during local search and can consume solve time on large instances.