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

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

doi:10.1016/j.trb.2023.02.016

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

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

^*Corresponding author for this work

Department of Technology and Operations Management

Eindhoven University of Technology
University of Lausanne
HEC Montreal
University of Bath

Research output: Contribution to journal › Article › Academic › peer-review

28 Citations (Scopus)

82 Downloads (Pure)

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.

Original language	English
Pages (from-to)	194-220
Number of pages	27
Journal	Transportation Research Part B: Methodological
Volume	170
DOIs	https://doi.org/10.1016/j.trb.2023.02.016
Publication status	Published - Apr 2023

Bibliographical note

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.

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.

Publisher Copyright:
© 2023

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10.1016/j.trb.2023.02.016

1-s2.0-S0191261523000358-mainFinal published version, 1.61 MBLicence: CC BY

Cite this

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

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.",

author = "{\"O}zarık, \{Sami Serkan\} and Virginie Lurkin and Veelenturf, \{Lucas P.\} and \{Van Woensel\}, Tom and Gilbert Laporte",

note = "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. 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. Publisher Copyright: {\textcopyright} 2023",

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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.

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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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