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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.

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Repository
NVIDIA/cuopt
Letzte Quellaktivität
15. Oktober 2026 um 00:14
Erkannte Sprache von SKILL.md
Englisch
Sterne
1.075
Forks
247

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
cuopt-routing-api-python
version
26.12.00
description
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
license
Apache-2.0
metadata
{"author":"NVIDIA cuOpt Team","tags":["cuopt","routing","vrp","tsp","python"]}
# 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 ```python 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 ```python # 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 ```python 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) ```python cost_matrix = cost_matrix.astype("float32") order_locations = cudf.Series([...], dtype="int32") demand = cudf.Series([...], dtype="int32") ``` ## Solver Settings ```python 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](references/examples.md) — VRP, PDP, multi-depot - [server_examples.md](references/server_examples.md) — REST client (curl, Python) - **Reference models:** This skill's `assets/` — [vrp_basic](assets/vrp_basic/), [pdp_basic](assets/pdp_basic/). See [assets/README.md](assets/README.md). ## Escalate For contribution or build-from-source, see the developer skill.
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