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Routing Electric Vehicles on Congested Street Networks

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  • Alexandre M. Florio

    (Center microelectronics de Provence Georges Charpak and Laboratoire informatique, modélisation et optimisation des systèmes, Centre national de la recherche scientifique unité mixte de recherche 6158, École des Mines de Saint-Étienne, F-13541 Gardanne, France)

  • Nabil Absi

    (Center microelectronics de Provence Georges Charpak and Laboratoire informatique, modélisation et optimisation des systèmes, Centre national de la recherche scientifique unité mixte de recherche 6158, École des Mines de Saint-Étienne, F-13541 Gardanne, France)

  • Dominique Feillet

    (Center microelectronics de Provence Georges Charpak and Laboratoire informatique, modélisation et optimisation des systèmes, Centre national de la recherche scientifique unité mixte de recherche 6158, École des Mines de Saint-Étienne, F-13541 Gardanne, France)

Abstract

Freight distribution with electric vehicles (EVs) is a promising alternative to reduce the carbon footprint associated with city logistics. Algorithms for planning routes for EVs should take into account their relatively short driving range and the effects of traffic congestion on the battery consumption. This paper proposes new methodology and illustrates how it can be applied to solve an electric vehicle routing problem with stochastic and time-dependent travel times where battery recharging along routes is not allowed. First, a new method for generating network-consistent (correlated in time and space) and time-dependent speed scenarios is introduced. Second, a new technique for applying branch and price on instances defined on real street networks is developed. Computational experiments demonstrate the effectiveness of the approach for finding optimal or near-optimal solutions in instances with up to 133 customers and almost 1,500 road links. With a high probability, the routes in the obtained solutions can be performed by EVs without requiring intermediate recharging stops. An execution time control policy to further reduce the chances of stranded EVs is also presented. In addition, we measure the cost of independence , which is the impact on solution feasibility when travel times are assumed statistically independent. Last, we give directions on how to extend the proposed framework to handle recourse actions.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ortrsc:v:55:y:2021:i:1:p:238-256
    DOI: 10.1287/trsc.2020.1004
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    3. Stanisław Iwan & Mariusz Nürnberg & Artur Bejger & Kinga Kijewska & Krzysztof Małecki, 2021. "Unloading Bays as Charging Stations for EFV-Based Urban Freight Delivery System—Example of Szczecin," Energies, MDPI, vol. 14(18), pages 1-22, September.
    4. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    5. Florio, Alexandre M. & Gendreau, Michel & Hartl, Richard F. & Minner, Stefan & Vidal, Thibaut, 2023. "Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1081-1093.
    6. Tomáš Settey & Jozef Gnap & František Synák & Tomáš Skrúcaný & Marek Dočkalik, 2021. "Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    7. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.

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