cuopt-routing-api-python

por nvidia

Roteamento de veículos (VRP, TSP, PDP) com cuOpt — apenas API Python. Use quando o usuário estiver construindo ou resolvendo roteamento em Python.

npx skills add https://github.com/nvidia/skills --skill cuopt-routing-api-python

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cuOpt Routing — Python API

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

Required questions

Ask these if not already clear:

Problem type — TSP, VRP, or PDP?
Locations — How many? Depot(s)? Cost or distance between pairs (matrix or derived)?
Orders / tasks — Which locations must be visited? Demand or service per stop?
Fleet — Number of vehicles, capacity per vehicle (and per dimension if multiple), start/end locations?
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

Problem	Fix
Empty solution	Widen time windows or check travel times
Infeasible orders	Increase fleet or capacity
Status != 0 with time windows	Add `add_transit_time_matrix()`
Wrong cost	Check cost_matrix is symmetric
`compute_waypoint_sequence` alters route_df	It 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

examples.md — VRP, PDP, multi-depot
server_examples.md — REST client (curl, Python)
Reference models: This skill's assets/ — vrp_basic, pdp_basic. See assets/README.md.