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Electric Vehicle Routing with Public Charging Stations

Author

Listed:
  • Nicholas D. Kullman

    (Universitè de Tours, LIFAT EA 6300, CNRS, ROOT ERL CNRS 7002, 37200 Tours, France)

  • Justin C. Goodson

    (Richard A. Chaifetz School of Business, Saint Louis University, St. Louis, Missouri 63103)

  • 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)

Abstract

We introduce the electric vehicle routing problem with public-private recharging strategy in which vehicles may recharge en route at public charging infrastructure as well as at a privately-owned depot. To hedge against uncertain demand at public charging stations, we design routing policies that anticipate station queue dynamics. We leverage a decomposition to identify good routing policies, including the optimal static policy and fixed-route-based rollout policies that dynamically respond to observed queues. The decomposition also enables us to establish dual bounds, providing a measure of goodness for our routing policies. In computational experiments using real instances from industry, we show the value of our policies to be within 10% of a dual bound. Furthermore, we demonstrate that our policies significantly outperform the industry-standard routing strategy in which vehicle recharging generally occurs at a central depot. Our methods stand to reduce the operating costs associated with electric vehicles, facilitating the transition from internal-combustion engine vehicles.

Suggested Citation

  • Nicholas D. Kullman & Justin C. Goodson & Jorge E. Mendoza, 2021. "Electric Vehicle Routing with Public Charging Stations," Transportation Science, INFORMS, vol. 55(3), pages 637-659, May.
  • Handle: RePEc:inm:ortrsc:v:55:y:2021:i:3:p:637-659
    DOI: 10.1287/trsc.2020.1018
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    References listed on IDEAS

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    Cited by:

    1. Remigiusz Iwańkowicz, 2021. "Effective Permutation Encoding for Evolutionary Optimization of the Electric Vehicle Routing Problem," Energies, MDPI, vol. 14(20), pages 1-18, October.

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