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Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence

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  • Özarık, Sami Serkan
  • Veelenturf, Lucas P.
  • Woensel, Tom Van
  • Laporte, Gilbert

Abstract

The recent increase in online orders in e-commerce leads to logistical challenges such as low hit rates (proportion of successful deliveries). We consider last-mile vehicle routing and scheduling problems in which customer presence probability data are taken into account. The aim is to reduce the expected cost resulting from low hit rates by considering both routing and scheduling decisions simultaneously in the planning phase. We model the problem and solve it by the means of an adaptive large neighborhood search metaheuristic which iterates between the routing and scheduling components of the problem. Computational experiments indicate that using customer-related presence data significantly can yield savings as large as 40% in system-wide costs compared with those of traditional vehicle routing solutions.

Suggested Citation

  • Özarık, Sami Serkan & Veelenturf, Lucas P. & Woensel, Tom Van & Laporte, Gilbert, 2021. "Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:transe:v:148:y:2021:i:c:s1366554521000399
    DOI: 10.1016/j.tre.2021.102263
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    Cited by:

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    2. Rhandal Masteguim & Claudio B. Cunha, 2022. "An Optimization-Based Approach to Evaluate the Operational and Environmental Impacts of Pick-Up Points on E-Commerce Urban Last-Mile Distribution: A Case Study in São Paulo, Brazil," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    3. Özarık, Sami Serkan & Lurkin, Virginie & Veelenturf, Lucas P. & Van Woensel, Tom & Laporte, Gilbert, 2023. "An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 194-220.
    4. Yin, Jiateng & Wang, Miao & D’Ariano, Andrea & Zhang, Jinlei & Yang, Lixing, 2023. "Synchronization of train timetables in an urban rail network: A bi-objective optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    5. Pegado-Bardayo, Ana & Lorenzo-Espejo, Antonio & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo, 2023. "A data-driven decision support system for service completion prediction in last mile logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    6. Yang, Dingtong & Hyland, Michael F. & Jayakrishnan, R., 2024. "Tackling the crowdsourced shared-trip delivery problem at scale with a novel decomposition heuristic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    7. Ji, Bin & Zhang, Dezhi & Zhang, Zheng & Yu, Samson S. & Van Woensel, Tom, 2022. "The generalized serial-lock scheduling problem on inland waterway: A novel decomposition-based solution framework and efficient heuristic approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).

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