A strategy for reducing the computational complexity of local search-based methods for the vehicle routing problem

Zachariadis, EE; Kiranoudis, CT

dc.contributor.author	Zachariadis, EE	en
dc.contributor.author	Kiranoudis, CT	en
dc.date.accessioned	2014-03-01T01:32:33Z
dc.date.available	2014-03-01T01:32:33Z
dc.date.issued	2010	en
dc.identifier.issn	0305-0548	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20176
dc.subject	Combinatorial optimization	en
dc.subject	Computational complexity	en
dc.subject	Local search	en
dc.subject	Vehicle routing	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Industrial	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Algorithmic aspects	en
dc.subject.other	Combinatorial optimization problems	en
dc.subject.other	Complexity reduction	en
dc.subject.other	Descriptors	en
dc.subject.other	Geographic distribution	en
dc.subject.other	Large sizes	en
dc.subject.other	Local search	en
dc.subject.other	Local search implementation	en
dc.subject.other	Local search method	en
dc.subject.other	Local search operators	en
dc.subject.other	Major cities	en
dc.subject.other	Metaheuristic	en
dc.subject.other	Metaheuristic strategy	en
dc.subject.other	New solutions	en
dc.subject.other	Real-world	en
dc.subject.other	Vehicle routing problem	en
dc.subject.other	Vehicle Routing Problems	en
dc.subject.other	Combinatorial optimization	en
dc.subject.other	Computational methods	en
dc.subject.other	Data structures	en
dc.subject.other	Heuristic methods	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Routing algorithms	en
dc.subject.other	Vehicles	en
dc.subject.other	Computational complexity	en
dc.title	A strategy for reducing the computational complexity of local search-based methods for the vehicle routing problem	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cor.2010.02.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cor.2010.02.009	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	This article focuses on the mechanism of evaluating solution neighborhoods, an algorithmic aspect which plays a crucial role on the efficiency of local-search based approaches. In specific, it presents a strategy for reducing the computational complexity required for applying local search to tackle various combinatorial optimization problems. The value of this contribution is two-fold. It helps practitioners design efficient local search implementations, and it facilitates the application of robust commercial local search-based algorithms to practical instances of very large size. The central rationale underlying the proposed complexity reduction strategy is straightforward: when a local search operator is applied to a given solution, only a limited part of this solution is modified. Thus, to exhaustively examine the neighborhood of the new solution, only the tentative moves that refer to the modified solution part have to be evaluated. To reduce the complexity of neighborhood evaluation, the static move descriptor (SMD) data structures are introduced, which encode local search moves in a systematic and solution independent manner. The proposed strategy is applied to the vehicle routing problem (VRP) which is of high importance both from the practical and theoretical viewpoints. The use of the SMD concept, for encoding three commonly applied quadratic local search operators, results into a VRP local search method which exhibits an almost linearithmic complexity in respect to the instance size. Furthermore. exploiting the SMD representation of tentative moves, a metaheuristic strategy is proposed, which is aimed at diversifying the conducted search via a simple penalization policy. The proposed metaheuristic was tested on various large and very large scale VRP benchmark instances. It produced fine results, and managed to improve several best known solutions. The method was also executed on real-world instances of 3000 customers, the data of which reflects the actual geographic distribution of customers within four major cities. (C) 2010 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computers and Operations Research	en
dc.identifier.doi	10.1016/j.cor.2010.02.009	en
dc.identifier.isi	ISI:000279096100005	en
dc.identifier.volume	37	en
dc.identifier.issue	12	en
dc.identifier.spage	2089	en
dc.identifier.epage	2105	en