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An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits

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

Abstract

Attended Home Delivery, where customer attendance at home is required, is an essential last-mile delivery challenge, e.g., for valuable, perishable, or oversized items. Logistics service providers are often faced no-show customers. In this paper, we consider the delivery problem in which customers can be revisited on the same day by a courier in the case of a failed first delivery attempt. Specifically, customer presence uncertainty is considered in a two-stage stochastic program, where penalties are introduced as recourse actions for failed deliveries. We build on the notion of a customer availability profile defined as a profile containing historical time-varying probability information of successful deliveries. We tackle this stochastic program by developing an efficient parallelized Adaptive Large Neighborhood Search algorithm. Our results show that by achieving a right balance between increasing the hit rate and reducing travel cost, logistics service providers can realize costs savings as high as 32% if they plan for second visits on the same day.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:170:y:2023:i:c:p:194-220
    DOI: 10.1016/j.trb.2023.02.016
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