IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v33y2021i4p1277-1283.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2020.1035
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijoc.2020.1035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Majid Salavati-Khoshghalb & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A hybrid recourse policy for the vehicle routing problem with stochastic demands," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 269-298, September.
    2. 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.
    3. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amin Abbasi-Pooya & Michael T. Lash, 2024. "The third party logistics provider freight management problem: a framework and deep reinforcement learning approach," Annals of Operations Research, Springer, vol. 339(1), pages 965-1024, August.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamid R. Sayarshad & Vahid Mahmoodian & Nebojša Bojović, 2021. "Dynamic Inventory Routing and Pricing Problem with a Mixed Fleet of Electric and Conventional Urban Freight Vehicles," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    2. Alexandre M. Florio & Nabil Absi & Dominique Feillet, 2021. "Routing Electric Vehicles on Congested Street Networks," Transportation Science, INFORMS, vol. 55(1), pages 238-256, 1-2.
    3. Alvo, Matías & Angulo, Gustavo & Klapp, Mathias A., 2021. "An exact solution approach for an electric bus dispatch problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    4. Sergio Maria Patella & Gianluca Grazieschi & Valerio Gatta & Edoardo Marcucci & Stefano Carrese, 2020. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review," Sustainability, MDPI, vol. 13(1), pages 1-29, December.
    5. Lai, Kexing & Chen, Tao & Natarajan, Balasubramaniam, 2020. "Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation," Energy, Elsevier, vol. 204(C).
    6. Maximiliano Cubillos & Mauro Dell’Amico & Ola Jabali & Federico Malucelli & Emanuele Tresoldi, 2023. "An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences," Energies, MDPI, vol. 16(10), pages 1-19, May.
    7. Erfan Ghorbani & Mahdi Alinaghian & Gevork. B. Gharehpetian & Sajad Mohammadi & Guido Perboli, 2020. "A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification," Sustainability, MDPI, vol. 12(21), pages 1-71, October.
    8. Cortés-Murcia, David L. & Prodhon, Caroline & Murat Afsar, H., 2019. "The electric vehicle routing problem with time windows, partial recharges and satellite customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 184-206.
    9. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    10. Azra Ghobadi & Mohammad Fallah & Reza Tavakkoli-Moghaddam & Hamed Kazemipoor, 2022. "A Fuzzy Two-Echelon Model to Optimize Energy Consumption in an Urban Logistics Network with Electric Vehicles," Sustainability, MDPI, vol. 14(21), pages 1-31, October.
    11. Wang, Mengtong & Miao, Lixin & Zhang, Canrong, 2021. "A branch-and-price algorithm for a green location routing problem with multi-type charging infrastructure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    12. Xu, Min & Meng, Qiang, 2019. "Fleet sizing for one-way electric carsharing services considering dynamic vehicle relocation and nonlinear charging profile," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 23-49.
    13. Schiffer, Maximilian & Walther, Grit, 2018. "Strategic planning of electric logistics fleet networks: A robust location-routing approach," Omega, Elsevier, vol. 80(C), pages 31-42.
    14. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2018. "Charge scheduling for electric freight vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 246-269.
    15. Goeke, Dominik, 2019. "Granular tabu search for the pickup and delivery problem with time windows and electric vehicles," European Journal of Operational Research, Elsevier, vol. 278(3), pages 821-836.
    16. Vidal, Thibaut & Laporte, Gilbert & Matl, Piotr, 2020. "A concise guide to existing and emerging vehicle routing problem variants," European Journal of Operational Research, Elsevier, vol. 286(2), pages 401-416.
    17. Nolz, Pamela C. & Absi, Nabil & Feillet, Dominique & Seragiotto, Clóvis, 2022. "The consistent electric-Vehicle routing problem with backhauls and charging management," European Journal of Operational Research, Elsevier, vol. 302(2), pages 700-716.
    18. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    19. Schiffer, Maximilian & Schneider, Michael & Laporte, Gilbert, 2018. "Designing sustainable mid-haul logistics networks with intra-route multi-resource facilities," European Journal of Operational Research, Elsevier, vol. 265(2), pages 517-532.
    20. Virginia Casella & Daniel Fernandez Valderrama & Giulio Ferro & Riccardo Minciardi & Massimo Paolucci & Luca Parodi & Michela Robba, 2022. "Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management," Energies, MDPI, vol. 15(11), pages 1-23, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:33:y:2021:i:4:p:1277-1283. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.