Chen, Yujie, Cowling, Peter Ivan orcid.org/0000-0003-1310-6683, Polack, Fiona A C orcid.org/0000-0001-7954-6433 et al. (1 more author) (2016) A multi-arm bandit neighbourhood search for routing and scheduling problems. In: UNSPECIFIED.
Abstract
Abstract Local search based meta-heuristics such as variable neighbourhood search have achieved remarkable success in solving complex combinatorial problems. Local search techniques are becoming increasingly popular and are used in a wide variety of meta-heuristics, such as genetic algorithms. Typically, local search iteratively improves a solution by making a series of small moves. Traditionally these methods do not employ any learning mechanism. We treat the selection of a local search neighbourhood as a dynamic multi- armed bandit (D-MAB) problem where learning techniques for solving the D-MAB can be used to guide the local search process. We present a D-MAB neighbourhood search (D-MABNS) which can be embedded within any meta- heuristic or hyperheuristic framework. Given a set of neighbourhoods, the aim of D-MABNS is to adapt the search sequence, testing promising solutions rst. We demonstrate the eectiveness of D-MABNS on two vehicle routing and scheduling problems, the real-world geographically distributed mainte- nance problem (GDMP) and the periodic vehicle routing problem (PVRP). We present comparisons to benchmark instances and give a detailed analysis of parameters, performance and behaviour. Keywords Meta-heuristic Local search Vehicle routing
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 22 Mar 2017 10:00 |
Last Modified: | 03 Feb 2025 00:01 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113924 |