TY - JOUR
T1 - An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits
AU - Özarık, Sami Serkan
AU - Lurkin, Virginie
AU - Veelenturf, Lucas P.
AU - Van Woensel, Tom
AU - Laporte, Gilbert
N1 - Funding Information:
Gilbert Laporte was partially supported by the Canadian Natural Sciences and Engineering Research Council, Canada under grant 2015-06189 . This research was also funded by The Dutch Research Council (NWO), The Netherlands Data2Move project under grant 628.009.013 . This work was carried out on the Dutch national e-infrastructure with the support of the SURF Cooperative. These supports are greatly acknowledged. Thanks are due to the associate editor and to the referees for their valuable comments.
PY - 2023/4/30
Y1 - 2023/4/30
N2 - 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.
AB - 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.
KW - Adaptive Large Neighborhood Search
KW - Attended home delivery
KW - Customer availability profile
KW - Last-mile delivery
KW - Routing
UR - http://www.scopus.com/inward/record.url?scp=85149364756&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2023.02.016
DO - 10.1016/j.trb.2023.02.016
M3 - Article
AN - SCOPUS:85149364756
SN - 0191-2615
VL - 170
SP - 194
EP - 220
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -