.. figure:: assets/images/PyVRP.svg :alt: PyVRP logo PyVRP is a Python package that offers a high-performance implementation of the hybrid genetic search algorithm for vehicle routing problems (VRPs). PyVRP currently supports the capacitated VRP (CVRP), the VRP with time windows (VRPTW), and prize-collecting. The implementation builds on `HGS-CVRP `_, but has been completely redesigned to be easy to use as a highly customisable Python package, while maintaining speed and state-of-the-art performance. This allows users to directly solve VRP instances, or implement variants of the HGS algorithm using Python, inspired by the examples in this documentation. Users can customise various aspects of the algorithm using Python, including population management, crossover strategies, granular neighbourhoods and operator selection in the local search. Additionally, for advanced use cases such as supporting additional VRP variants, users can build and install PyVRP directly from the source code. The PyVRP package comes with pre-compiled binaries for Windows, Mac OS and Linux, and can thus be easily installed without requiring local compilation. It can be installed through *pip* via .. code-block:: shell pip install pyvrp .. hint:: If you are new to vehicle routing or metaheuristics, you might benefit from first reading the :doc:`introduction to VRP ` and the :doc:`introduction to HGS ` pages. To set up an installation from source, or to run the examples listed below yourself, please have a look at the :doc:`installation instructions `. Contents -------- .. toctree:: :maxdepth: 1 :caption: Getting started setup/introduction_to_vrp setup/introduction_to_hgs setup/installation setup/getting_help setup/benchmarks .. toctree:: :maxdepth: 1 :caption: Examples examples/vrptw examples/cvrp .. toctree:: :maxdepth: 1 :caption: API reference api/pyvrp api/crossover api/diversity api/search api/plotting api/stop .. toctree:: :maxdepth: 1 :caption: Developing PyVRP dev/contributing dev/benchmarking