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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nicholas D. Kullman & Aurelien Froger & Jorge E. Mendoza & Justin C. Goodson, 2021. "frvcpy: An Open-Source Solver for the Fixed Route Vehicle Charging Problem," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1277-1283, October.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:4:p:1277-1283
    DOI: 10.1287/ijoc.2020.1035
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    References listed on IDEAS

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    3. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A Rule-Based Recourse for the Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 53(5), pages 1334-1353, September.
    4. Samuel Pelletier & Ola Jabali & Gilbert Laporte, 2016. "50th Anniversary Invited Article—Goods Distribution with Electric Vehicles: Review and Research Perspectives," Transportation Science, INFORMS, vol. 50(1), pages 3-22, February.
    5. Hiermann, Gerhard & Puchinger, Jakob & Ropke, Stefan & Hartl, Richard F., 2016. "The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 995-1018.
    6. Montoya, Alejandro & Guéret, Christelle & Mendoza, Jorge E. & Villegas, Juan G., 2017. "The electric vehicle routing problem with nonlinear charging function," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 87-110.
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    Cited by:

    1. Park, Hyunwoo & Lee, Chungmok, 2024. "An exact algorithm for maximum electric vehicle flow coverage problem with heterogeneous chargers, nonlinear charging time and route deviations," European Journal of Operational Research, Elsevier, vol. 315(3), pages 926-951.

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