frvcpy: An Open-Source Solver for the Fixed Route Vehicle Charging Problem

Nicholas D. Kullman (), Aurelien Froger (), Jorge E. Mendoza () and Justin C. Goodson ()
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Nicholas D. Kullman: Laboratory of Fundamental and Applied Computer Science (LIFAT), Université de Tours, 37200 Tours, France
Aurelien Froger: Institut de Mathématiques de Bordeaux, Université de Bordeaux, Inria Bordeaux Sud-Ouest, 33405 Talence, France
Jorge E. Mendoza: HEC Montréal, Montréal, Québec H3T 2A7, Canada; Centre Interuniversitaire de Recherche sur les Réseaux d’Entreprise, la Logistique et le Transport (CIRRELT), Montréal, Québec H3T 1J4, Canada
Justin C. Goodson: Richard A. Chaifetz School of Business, Saint Louis University, St. Louis, Missouri 63103

INFORMS Journal on Computing, 2021, vol. 33, issue 4, 1277-1283

Abstract: Electric vehicles offer a pathway to more sustainable transportation, but their adoption entails new challenges not faced by their petroleum-based counterparts. A difficult task in vehicle routing problems addressing these challenges is determining how to make good charging decisions for an electric vehicle traveling a given route. This is known as the fixed route vehicle charging problem. An exact and efficient algorithm for this task exists, but its implementation is sufficiently complex to deter researchers from adopting it. In this work we introduce frvcpy, an open-source Python package implementing this algorithm. Our aim with the package is to make it easier for researchers to solve electric vehicle routing problems, facilitating the development of optimization tools that may ultimately enable the mass adoption of electric vehicles. Summary of Contribution: This work describes a novel software tool for the vehicle routing community. The tool, frvcpy, addresses one of the primary challenges faced by the vehicle routing community when considering problems involving the adoption of electric vehicles (EVs): how to make optimal charging decisions. The state-of-the-art algorithm for solving these problems is sufficiently complex to deter researchers from using it, leading them to adopt less robust methods. frvcpy offers an easy-to-use, lightweight implementation of this algorithm, providing optimal solutions in low (∼5 ms) runtime. It is designed to be easily embedded in larger solution schemes for general EV routing problems, requiring minimal input, offering compatibility with the community standard file types, and offering access both through the command line and a Python API. The tool has thus far proven adaptable, having been used by researchers studying EV routing problems with novel constraints. Our aim with frvcpy is to make it easier for researchers to solve EV routing problems, facilitating the development of optimization tools that may contribute toward the mass adoption of electric vehicles.

Keywords: electric vehicles; vehicle routing; open-source; Python; exact optimization; labeling algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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