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A study on flow decomposition methods for scheduling of electric buses in public transport based on aggregated time–space network models

Author

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  • Nils Olsen

    (Freie Universität Berlin)

  • Natalia Kliewer

    (Freie Universität Berlin)

  • Lena Wolbeck

    (Freie Universität Berlin)

Abstract

Over the past few years, many public transport companies have launched pilot projects testing the operation of electric buses. The basic objective of these projects is to substitute diesel buses with electric buses within the companies’ daily operations. Despite an extensive media coverage, the share of electric buses deployed still remains very small in practice. In this context, new challenges arise for a company’s planning process due to the considerably shorter ranges of electric buses compared to traditional combustion engine buses and to the necessity to recharge their batteries at charging stations. Vehicle scheduling, an essential planning task within the planning process, is especially affected by these additional challenges. In this paper, we define the mixed fleet vehicle scheduling problem with electric vehicles. We extend the traditional vehicle scheduling problem by considering a mixed fleet consisting of electric buses with limited driving ranges and rechargeable batteries as well as traditional diesel buses without such range limitations. To solve the problem, we introduce a three-phase solution approach based on an aggregated time–space network consisting of an exact solution method for the vehicle scheduling problem without range limitations, innovative flow decomposition methods, and a novel algorithm for the consideration of charging procedures. Through a computational study using real-world bus timetables, we show that our solution approach meets the requirements of a first application of electric buses in practice. Since the employment of electric buses is mainly influenced by the availability of charging infrastructure, which is determined by the distribution of charging stations within the route network, we particularly focus on the influence of the charging infrastructure.

Suggested Citation

  • Nils Olsen & Natalia Kliewer & Lena Wolbeck, 2022. "A study on flow decomposition methods for scheduling of electric buses in public transport based on aggregated time–space network models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 883-919, September.
    • Handle: RePEc:spr:cejnor:v:30:y:2022:i:3:d:10.1007_s10100-020-00705-6
    DOI: 10.1007/s10100-020-00705-6
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    References listed on IDEAS

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    1. Kliewer, Natalia & Mellouli, Taieb & Suhl, Leena, 2006. "A time-space network based exact optimization model for multi-depot bus scheduling," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1616-1627, December.
    2. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    3. Natalia Kliewer & Vitali Gintner & Leena Suhl, 2008. "Line Change Considerations Within a Time-Space Network Based Multi-Depot Bus Scheduling Model," Lecture Notes in Economics and Mathematical Systems, in: Mark Hickman & Pitu Mirchandani & Stefan Voß (ed.), Computer-aided Systems in Public Transport, pages 57-70, Springer.
    4. 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.
    5. Haghani, Ali & Banihashemi, Mohamadreza, 2002. "Heuristic approaches for solving large-scale bus transit vehicle scheduling problem with route time constraints," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(4), pages 309-333, May.
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