PathWyse: a flexible, open-source library for the resource constrained shortest path problem

Abstract

In this paper, we consider a fundamental and hard combinatorial problem: the Resource Constrained Shortest Path Problem (RCSPP). We describe the implementation of a flexible, open-source library for the solution of the RCSPP, called PathWyse, capable of tackling several variants of the problem. We designed PathWyse with the final user in mind, developing easy-to-use interfaces without compromising performance. We provide computational experiments on three classes of instances of the RCSPP, namely RCSPP on cyclic networks, RCSPP on large acyclic networks, and RCSPP on ad-hoc cyclic networks. We show that PathWyse is packed off-the-shelf with algorithms capable of tackling generic problems, and can exploit dedicated algorithms for specific classes. This paper represents the first step along the journey of devising and implementing a comprehensive open-source library for a large variety of RCSPPs. The current version of the library carries exact algorithms for the RCSPP but new algorithms, both heuristic and exact, will be added thanks to the flexible design. We also foresee PathWyse becoming a platform ready for data-driven and process-driven methodologies for these types of problems.

Keywords:

• Resource constrained shortest path
• open source library
• dynamic programming

AMS Subject Classifications:

• 05-04
• 05-08
• 05C38
• 90C39

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Matteo Salani

Matteo Salani is a senior researcher at IDSIA. He obtained his PhD in Computer Science in 2006 at the University of Milan. His research focuses on combinatorial optimization, exploring exact and heuristic algorithms. He is also involved in applied research in different fields: management, energy, transportation and telecommunication.

Saverio Basso

Saverio Basso is a postdoctoral researcher at SUPSI. He obtained his MSc in 2017 and received his PhD in Computer Science in 2021 at the University of Milan. His research interests lie in exact and heuristic algorithms for combinatorial problems, decomposition methods, and data analytics.

Vincenzo Giuffrida

Vincenzo Giuffrida earned a BSc in Computer Science from the University of Milan in 2009, followed by a MSc in Computer Science from the same institution in 2012. Since 2019, he has been a researcher at IDSIA, focusing on applied research projects in the field of artificial intelligence.