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cuopt-routing-api-python

Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

The English summary is being prepared. Showing the original description.

See how to install

Files included (12)

  • SKILL.md3.6 KB
  • assets/pdp_basic/model.py1.5 KB
  • assets/pdp_basic/README.md257 B
  • assets/README.md553 B
  • assets/vrp_basic/model.py838 B
  • assets/vrp_basic/README.md272 B
  • BENCHMARK.md4.6 KB
  • evals/evals.json8.7 KB
  • references/examples.md6.9 KB
  • references/server_examples.md5.7 KB
  • skill-card.md4.0 KB
  • skill.oms.sig6.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

cuOpt Routing — Python API

This skill is Python only. Routing has no C API in cuOpt.

Required questions

Ask these if not already clear:

  1. Problem type — TSP, VRP, or PDP?
  2. Locations — How many? Depot(s)? Cost or distance between pairs (matrix or derived)?
  3. Orders / tasks — Which locations must be visited? Demand or service per stop?
  4. Fleet — Number of vehicles, capacity per vehicle (and per dimension if multiple), start/end locations?
  5. Constraints — Time windows (earliest/latest arrival), service times, precedence (order A before B)?

Minimal VRP Example

import cudf
from cuopt import routing

cost_matrix = cudf.DataFrame([...], dtype="float32")
dm = routing.DataModel(n_locations=4, n_fleet=2, n_orders=3)
dm.add_cost_matrix(cost_matrix)
dm.set_order_locations(cudf.Series([1, 2, 3], dtype="int32"))
solution = routing.Solve(dm, routing.SolverSettings())

if solution.get_status() == 0:
    solution.display_routes()

Adding Constraints

# Time windows
dm.add_transit_time_matrix(transit_time_matrix)
dm.set_order_time_windows(earliest_series, latest_series)

# Capacities
dm.add_capacity_dimension("weight", demand_series, capacity_series)
dm.set_order_service_times(service_times)
dm.set_vehicle_locations(start_locations, end_locations)
dm.set_vehicle_time_windows(earliest_start, latest_return)

# Pickup-delivery pairs
dm.set_pickup_delivery_pairs(pickup_indices, delivery_indices)

# Precedence
dm.add_order_precedence(node_id=2, preceding_nodes=np.array([0, 1]))

Solution Checking

status = solution.get_status()  # 0=SUCCESS, 1=FAIL, 2=TIMEOUT, 3=EMPTY
if status == 0:
    route_df = solution.get_route()
    total_cost = solution.get_total_objective()
else:
    print(solution.get_error_message())
    print(solution.get_infeasible_orders().to_list())

Data Types (use explicit dtypes)

cost_matrix = cost_matrix.astype("float32")
order_locations = cudf.Series([...], dtype="int32")
demand = cudf.Series([...], dtype="int32")

Solver Settings

ss = routing.SolverSettings()
ss.set_time_limit(30)
ss.set_verbose_mode(True)
ss.set_error_logging_mode(True)

Common Issues

ProblemFix
Empty solutionWiden time windows or check travel times
Infeasible ordersIncrease fleet or capacity
Status != 0 with time windowsAdd add_transit_time_matrix()
Wrong costCheck cost_matrix is symmetric
compute_waypoint_sequence alters route_dfIt replaces the location column with waypoint ids in place — pass route_df.copy() if you still need cost-matrix indices (e.g. when iterating per truck)

Debugging

When status != 0: print(solution.get_error_message()) and print(solution.get_infeasible_orders().to_list()) to see which orders are infeasible.

Data types: Use explicit dtypes (float32, int32) for matrices and series to avoid silent errors.

Examples

Escalate

For contribution or build-from-source, see the developer skill.

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