Adaptive large variable neighborhood search for a multiperiod vehicle and technician routing problem

DC FieldValueLanguage
dc.contributor.authorGraf, Benjamin
dc.date.accessioned2021-12-23T16:02:37Z-
dc.date.available2021-12-23T16:02:37Z-
dc.date.issued2020
dc.identifier.issn00283045
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/5519-
dc.description.abstractThe VeRoLog Solver Challenge 2018-2019 of the EURO working groupvehicle routing and logistics(VeRoLog) considers a multiperiod vehicle and technician routing and scheduling problem. This paper proposes a combination of large neighborhood and local search heuristics and a decomposition approach to efficiently generate competitive solutions under restricted computational resources. The interplay of the heuristics, the decomposition, and the way the search space is explored are orchestrated by an adaptive layer that explicitly considers the instance to be solved, a time limit and the performance of the computing environment. In a computational study it is shown that the method is efficient and effective, especially under tight time restrictions.
dc.language.isoen
dc.publisherWILEY
dc.relation.ispartofNETWORKS
dc.subjectadaptive large neighborhood search
dc.subjectComputer Science
dc.subjectComputer Science, Hardware & Architecture
dc.subjectLOCAL SEARCH
dc.subjectmultiperiod VRP
dc.subjectOperations Research & Management Science
dc.subjecttechnician routing and scheduling
dc.subjectvariable neighborhood descent
dc.subjectVeRoLog Solver Challenge
dc.titleAdaptive large variable neighborhood search for a multiperiod vehicle and technician routing problem
dc.typejournal article
dc.identifier.doi10.1002/net.21959
dc.identifier.isiISI:000541537700001
dc.description.volume76
dc.description.issue2, SI
dc.description.startpage256
dc.description.endpage272
dc.contributor.orcid0000-0002-8382-4819
dc.identifier.eissn10970037
dc.publisher.place111 RIVER ST, HOBOKEN 07030-5774, NJ USA
dcterms.isPartOf.abbreviationNetworks
dcterms.oaStatushybrid
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