A hybrid guided local search for the vehicle-routing problem with intermediate replenishment facilities

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	1091-9856	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18540
dc.subject	Guided local search	en
dc.subject	Replenishment facilities	en
dc.subject	Tabu search	en
dc.subject	Vehicle routing	en
dc.subject	VNS	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Operations Research & Management Science	en
dc.title	A hybrid guided local search for the vehicle-routing problem with intermediate replenishment facilities	en
heal.type	journalArticle	en
heal.identifier.primary	10.1287/ijoc.l070.0230	en
heal.identifier.secondary	http://dx.doi.org/10.1287/ijoc.l070.0230	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	We propose a three-step algorithmic framework for solving a new variant of the vehicle-routing problem (VRP) called the vehicle-routing problem with intermediate replenishment facilities (VRPIRF). The aim of this problem is to determine optimal routes for a fleet of vehicles that can renew their capacity at intermediate replenishment stations. Although this problem is often met in real-life scenarios of transportation logistics, it has not received much attention by researchers. Our proposed framework employs a combination of algorithmic blocks based on powerful metaheuristic methodologies designed to achieve a desirable intensification and diversification interplay. In the first step of the solution approach, the initial solution is obtained by a cost-saving construction heuristic. In the second step, the initial solution is improved by employing tabu search within the variable neighborhood search methodology. Finally, guided local search is applied in the third step, to eliminate low-quality features from the final solution produced. The proposed algorithmic framework was successfully applied to benchmark instances in the literature, generating several new best solutions. To motivate the proposed algorithmic choices and test the robustness of the algorithm, we also developed new classes of VRPIRF benchmark instances with diverse problem characteristics. © 2008 INFORMS.	en
heal.publisher	INFORMS	en
heal.journalName	INFORMS Journal on Computing	en
dc.identifier.doi	10.1287/ijoc.l070.0230	en
dc.identifier.isi	ISI:000254140400015	en
dc.identifier.volume	20	en
dc.identifier.issue	1	en
dc.identifier.spage	154	en
dc.identifier.epage	168	en