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Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
npx skill4agent add nvidia/skills cuopt-routing-api-pythonimport 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()# 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]))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())cost_matrix = cost_matrix.astype("float32")
order_locations = cudf.Series([...], dtype="int32")
demand = cudf.Series([...], dtype="int32")ss = routing.SolverSettings()
ss.set_time_limit(30)
ss.set_verbose_mode(True)
ss.set_error_logging_mode(True)| Problem | Fix |
|---|---|
| Empty solution | Widen time windows or check travel times |
| Infeasible orders | Increase fleet or capacity |
| Status != 0 with time windows | Add |
| Wrong cost | Check cost_matrix is symmetric |
| It replaces the |
print(solution.get_error_message())print(solution.get_infeasible_orders().to_list())assets/