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Variable Neighbourhood Descent with Memory: A Hybrid Metaheuristic for Supermarket Resupply

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Publication details

Title of host publicationHybrid Metaheuristics - 10th International Workshop, HM 2016, Proceedings
DatePublished - 2016
Pages32-46
Number of pages15
PublisherSpringer-Verlag
EditorsAngelo Cangelosi, El-Ghazali Talbi, Christian Blum, Vincenzo Cutello, Mario Pavone, Maria J. Blesa, Alessandro di Nuovo
Volume9668
Original languageEnglish
ISBN (Print)978-3-319-39636-1

Publication series

NameLecture Notes in Computer Science
Volume9668
ISSN (Print)0302-9743

Abstract

Supermarket supply chains represent an area in which optimisation of vehicle routes and scheduling can lead to huge cost and environmental savings. As just-in-time ordering practices become more common, traditionally fixed resupply routes and schedules are increasingly unable to meet the demands of the supermarkets. Instead, we model this as a dynamic pickup and delivery problem with soft time windows (PDPSTW). We present the variable neighbourhood descent with memory (VNDM) hybrid metaheuristic (HM) and compare its performance against Q-learning (QL), binary exponential back off (BEBO) and random descent (RD) hyperheuristics on published benchmark and real-world instances of the PDPSTW. We find that VNDM consistently generates the highest quality solutions, with the fewest routes or shortest distances, amongst the methods tested. It is capable of finding the best known solutions to 55 of 176 published benchmarks as well as producing the best results on our real-world data set, supplied by Transfaction Ltd.

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