An efficient meta-heuristic algorithm for routing product collecting vehicles of dehydration plants. II. Algorithm performance and case studies

Tarantilis, CD; Kiranoudis, CT

dc.contributor.author	Tarantilis, CD	en
dc.contributor.author	Kiranoudis, CT	en
dc.date.accessioned	2014-03-01T01:16:08Z
dc.date.available	2014-03-01T01:16:08Z
dc.date.issued	2001	en
dc.identifier.issn	0737-3937	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13931
dc.subject	threshold-accepting	en
dc.subject	meta-heuristics	en
dc.subject	Poisson probability distribution	en
dc.subject	combinatorial optimization	en
dc.subject	vehicle routing problem	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	ROUTEING PROBLEM	en
dc.title	An efficient meta-heuristic algorithm for routing product collecting vehicles of dehydration plants. II. Algorithm performance and case studies	en
heal.type	journalArticle	en
heal.identifier.primary	10.1081/DRT-100104801	en
heal.identifier.secondary	http://dx.doi.org/10.1081/DRT-100104801	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	Routing of vehicle fleet for collecting newly cropped raw materials for multi-product dehydration plants is a component of plant production schedule of utmost significance. A meta-heuristic algorithm for efficiently solving the collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configurations in linear search terms, which sweep the huge solution space in a guided and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the execution of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the optimization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algorithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedures of dehydration plants in Greece.	en
heal.publisher	MARCEL DEKKER INC	en
heal.journalName	DRYING TECHNOLOGY	en
dc.identifier.doi	10.1081/DRT-100104801	en
dc.identifier.isi	ISI:000170452900004	en
dc.identifier.volume	19	en
dc.identifier.issue	6	en
dc.identifier.spage	987	en
dc.identifier.epage	1004	en