PyVRP: a high-performance VRP solver package

• Published in: arXiv.org
• Main Authors: Wouda, Niels A, Lan, Leon, Kool, Wouter
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.03.2024
• Subjects: Algorithms; C++ (programming language); Computer Science - Learning; Computer Science - Neural and Evolutionary Computing; Critical components; Software engineering; Solvers; State of the art; Vehicle routing
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2403.13795

We introduce PyVRP, a Python package that implements hybrid genetic search in a state-of-the-art vehicle routing problem (VRP) solver. The package is designed for the VRP with time windows (VRPTW), but can be easily extended to support other VRP variants. PyVRP combines the flexibility of Python with the performance of C++, by implementing (only) performance critical parts of the algorithm in C++, while being fully customisable at the Python level. PyVRP is a polished implementation of the algorithm that ranked 1st in the 2021 DIMACS VRPTW challenge and, after improvements, ranked 1st on the static variant of the EURO meets NeurIPS 2022 vehicle routing competition. The code follows good software engineering practices, and is well-documented and unit tested. PyVRP is freely available under the liberal MIT license. Through numerical experiments we show that PyVRP achieves state-of-the-art results on the VRPTW and capacitated VRP. We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver.