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Large Neighborhood Search for Electric Vehicle Fleet Scheduling

Author

Listed:
  • Steffen Limmer

    (Honda Research Institute Europe GmbH, 63073 Offenbach, Germany)

  • Johannes Varga

    (Institute of Logic and Computation, Vienna University of Technology, 1040 Vienna, Austria)

  • Günther Robert Raidl

    (Institute of Logic and Computation, Vienna University of Technology, 1040 Vienna, Austria)

Abstract

This work considers the problem of planning how a fleet of shared electric vehicles is charged and used for serving a set of reservations. While exact approaches can be used to efficiently solve small to medium-sized instances of this problem, heuristic approaches have been demonstrated to be superior in larger instances. The present work proposes a large neighborhood search approach for solving this problem, which employs a mixed integer linear programming-based repair operator. Three variants of the approach using different destroy operators are evaluated on large instances of the problem. The experimental results show that the proposed approach significantly outperforms earlier state-of-the-art methods on this benchmark set by obtaining solutions with up to 8.5% better objective values.

Suggested Citation

  • Steffen Limmer & Johannes Varga & Günther Robert Raidl, 2023. "Large Neighborhood Search for Electric Vehicle Fleet Scheduling," Energies, MDPI, vol. 16(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4576-:d:1166120
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    References listed on IDEAS

    as
    1. Guy Desaulniers & Fausto Errico & Stefan Irnich & Michael Schneider, 2016. "Exact Algorithms for Electric Vehicle-Routing Problems with Time Windows," Operations Research, INFORMS, vol. 64(6), pages 1388-1405, December.
    2. Bongiovanni, Claudia & Kaspi, Mor & Geroliminis, Nikolas, 2019. "The electric autonomous dial-a-ride problem," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 436-456.
    3. Benjamin Schaden & Thomas Jatschka & Steffen Limmer & Günther Robert Raidl, 2021. "Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers," Energies, MDPI, vol. 14(22), pages 1-33, November.
    4. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    5. Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.
    6. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Su, Yue & Dupin, Nicolas & Puchinger, Jakob, 2023. "A deterministic annealing local search for the electric autonomous dial-a-ride problem," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1091-1111.
    8. Ilham Naharudinsyah & Steffen Limmer, 2018. "Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market," Energies, MDPI, vol. 11(6), pages 1-12, June.
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