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Recovery management for a dial-a-ride system with real-time disruptions

Author

Listed:
  • Paquay, Célia
  • Crama, Yves
  • Pironet, Thierry

Abstract

The problem considered in this work stems from a non-profit organization in charge of door-to-door passenger transportation for medical appointments. Patients are picked up at home by a driver and are then dropped at their appointment location. They may also be driven back home at the end of their appointment. Some patients have specific requirements, e.g., they may require an accompanying person or a wheelchair. Planning such activities gives rise to a so-called dial-a-ride problem. In the present work, it is assumed that the requests assigned to the drivers have been selected, and the transportation plan has been established for the next day. However, in practice, appointment durations may vary due to unforeseen circumstances, and some transportation requests may be modified, delayed or canceled during the day. The aim of this work is to propose a reactive algorithm which can adapt the initial plan in order to manage the disruptions and to take care of as many patients as possible in real-time. The plan should be modified quickly when a perturbation is observed, without resorting to major changes which may confuse the drivers and the patients. Several recourse procedures are defined for this purpose. They allow the dispatcher to temporarily delete a request, to insert a previously deleted request, or to permanently cancel a request. Simulation techniques are used to test the approach on randomly generated scenarios. Several key performance indicators are introduced in order to measure the impact of the disruptions and the quality of the solutions.

Suggested Citation

  • Paquay, Célia & Crama, Yves & Pironet, Thierry, 2020. "Recovery management for a dial-a-ride system with real-time disruptions," European Journal of Operational Research, Elsevier, vol. 280(3), pages 953-969.
  • Handle: RePEc:eee:ejores:v:280:y:2020:i:3:p:953-969
    DOI: 10.1016/j.ejor.2019.08.006
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    Citations

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    Cited by:

    1. Yan, Pengyu & Yu, Kaize & Chao, Xiuli & Chen, Zhibin, 2023. "An online reinforcement learning approach to charging and order-dispatching optimization for an e-hailing electric vehicle fleet," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1218-1233.
    2. Lian, Ying & Lucas, Flavien & Sörensen, Kenneth, 2024. "Prepositioning can improve the performance of a dynamic stochastic on-demand public bus system," European Journal of Operational Research, Elsevier, vol. 312(1), pages 338-356.
    3. Zhang, Zhenyu & Ji, Tingting & Wei, Hsi-Hsien, 2022. "Dynamic emergency inspection routing and restoration scheduling to enhance the post-earthquake resilience of a highway–bridge network," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Sinha, Priyank & Kumar, Sameer & Chandra, Charu, 2023. "Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 304(1), pages 339-352.
    5. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef, 2021. "Real-time ride-sharing framework with dynamic timeframe and anticipation-based migration," European Journal of Operational Research, Elsevier, vol. 288(3), pages 810-828.
    6. Molenbruch, Yves & Braekers, Kris & Hirsch, Patrick & Oberscheider, Marco, 2021. "Analyzing the benefits of an integrated mobility system using a matheuristic routing algorithm," European Journal of Operational Research, Elsevier, vol. 290(1), pages 81-98.

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