A guided tabu search for the heterogeneous vehicle routeing problem

Tarantilis, CD; Zachariadis, EE; Kiranoudis, CT

dc.contributor.author	Tarantilis, CD	en
dc.contributor.author	Zachariadis, EE	en
dc.contributor.author	Kiranoudis, CT	en
dc.date.accessioned	2014-03-01T01:27:41Z
dc.date.available	2014-03-01T01:27:41Z
dc.date.issued	2008	en
dc.identifier.issn	0160-5682	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18539
dc.subject	Distribution	en
dc.subject	Heuristics	en
dc.subject	Logistics	en
dc.subject	Vehicle routeing problem	en
dc.subject.classification	Management	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Cost accounting	en
dc.subject.other	Tabu search	en
dc.subject.other	Vehicles	en
dc.subject.other	Benchmark problems	en
dc.subject.other	Carrying loads	en
dc.subject.other	Computational times	en
dc.subject.other	Distance units	en
dc.subject.other	Distribution	en
dc.subject.other	Fleet compositions	en
dc.subject.other	Guiding strategies	en
dc.subject.other	Heterogeneous vehicles	en
dc.subject.other	Heuristics	en
dc.subject.other	Minimum costs	en
dc.subject.other	Objective functions	en
dc.subject.other	Variable costs	en
dc.subject.other	Fleet operations	en
dc.title	A guided tabu search for the heterogeneous vehicle routeing problem	en
heal.type	journalArticle	en
heal.identifier.primary	10.1057/palgrave.jors.2602504	en
heal.identifier.secondary	http://dx.doi.org/10.1057/palgrave.jors.2602504	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	The aim of this paper is to present a new algorithmic methodology for the heterogeneous fixed fleet vehicle routeing problem (HFFVRP). HFFVRP consists of determining the minimum cost routes for a fleet of vehicles in order to satisfy the demand of the customer population. The fleet composition is fixed and consists of various types of vehicles that differ with respect to their maximum carrying load and variable cost per distance unit. Our proposed algorithm called guided tabu search (GTS) is based on tabu search controlled by a continuous guiding mechanism that modifies the objective function of the problem. The role of this guiding strategy is to diversify the conducted search and help it overcome local optima encountered. The GTS method was applied successfully on HFFVRP benchmark problems producing best-known and new best-known solutions in short computational times. © 2008 Operational Research Society Ltd. All rights reserved.	en
heal.publisher	PALGRAVE MACMILLAN LTD	en
heal.journalName	Journal of the Operational Research Society	en
dc.identifier.doi	10.1057/palgrave.jors.2602504	en
dc.identifier.isi	ISI:000260925200009	en
dc.identifier.volume	59	en
dc.identifier.issue	12	en
dc.identifier.spage	1659	en
dc.identifier.epage	1673	en