PyVRP / PyVRP

PyVRP is an open-source, state-of-the-art vehicle routing problem (VRP) solver developed by RoutingLab. It currently supports VRPs with: • Pickups and deliveries between depots and clients (capacitated VRP, VRP with simultaneous pickup and delivery, VRP with backhaul); • Vehicles of different capacities, costs, shift durations, routing profiles, and maximum distance and duration constraints (heterogeneous fleet VRP, site-dependent VRP); • Time windows, client service durations, and release times (VRP with time windows and release times); • Multiple depots (multi-depot VRP); • Reloading along routes at different reload depots (multi-trip VRP); • Optional clients with prizes for visiting (prize collecting, team orienteering problem); • Client groups imposing additional restrictions on multiple clients jointly (generalised VRP, VRP with multiple time windows). PyVRP is available on the Python package index as . It may be installed in the usual way as The documentation is available [here][1]. > [!TIP] > Looking for professional support? RoutingLab provides consulting, custom development, and FastVRP - a production-ready route optimisation API built on PyVRP. Tutorials We provide many tutorials that show how to use PyVRP to solve vehicle routing problems. The [quickstart][4] introduces PyVRP's modelling interface and is a great way to get started. The following tutorials cover specific features in more detail: • [Load and vehicle capacities][5]: pickups, deliveries, and multiple load dimensions. • [Time and duration constraints][6]: time windows, service durations, release times, shifts and overtime. • [Profiles][7]: different distances and durations for different vehicle types, and modelling access restrictions. • [Optional clients][8]: using rewards to visit optional clients. • [Mutually exclusive groups][9]: modelling alternative services. • [Reloading][10]: vehicle reloading at depots during routes. For those interested in PyVRP's underlying algorithm, see [this page][11] for a high-level description of the iterated local search algorithm, and [this notebook][12] for an implementation of the method using PyVRP's components. Getting help Feel free to open an issue or a new discussion thread here on GitHub. When writing your issue or discussion, please follow the instructions [here][3]. For professional support, contact us at info@routinglab.tech. Contributing While we are very grateful for any contributions you are willing to make, reviewing and maintaining third-party code takes a significant amount of our time. Before you start working on your contribution, please have a look [here][2] to get started. Make sure to discuss the change first in a GitHub issue. Feel free to open a new one if no appropriate issue already exists! How to cite PyVRP If you use PyVRP in your research, please consider citing the following paper: > Wouda, N.A., L. Lan, and W. Kool (2024). > PyVRP: a high-performance VRP solver package. > _INFORMS Journal on Computing_, 36(4): 943-955. > https://doi.org/10.1287/ijoc.2023.0055 Or, using the following BibTeX entry: A preprint of this paper is available on [arXiv][13]. [1]: https://pyvrp.org/ [2]: https://pyvrp.org/dev/contributing.html [3]: https://pyvrp.org/setup/getting_help.html [4]: https://pyvrp.org/notebooks/quick_start.html [5]: https://pyvrp.org/notebooks/load.html [6]: https://pyvrp.org/notebooks/duration_constraints.html [7]: https://pyvrp.org/notebooks/profiles.html [8]: https://pyvrp.org/notebooks/optional_clients.html [9]: https://pyvrp.org/notebooks/mutually_exclusive_groups.html [10]: https://pyvrp.org/notebooks/reloading.html [11]: https://pyvrp.org/dev/algorithm.html [12]: https://pyvrp.org/notebooks/pyvrp_implementation.html [13]: https://arxiv.org/abs/2403.13795