Very large scale vehicle routing with time windows and stochastic demand using genetic algorithms with parallel fitness evaluation

Protonotarios, M; Mourkousis, G; Vyridis, I; Varvarigou, T

dc.contributor.author	Protonotarios, M	en
dc.contributor.author	Mourkousis, G	en
dc.contributor.author	Vyridis, I	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T01:15:57Z
dc.date.available	2014-03-01T01:15:57Z
dc.date.issued	2000	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13851
dc.subject	Customer Satisfaction	en
dc.subject	Customer Service	en
dc.subject	Distribution Costs	en
dc.subject	Genetic Algorithm	en
dc.subject	High Performance Computer	en
dc.subject	Large Scale	en
dc.subject	Optimal Solution	en
dc.subject	Satisfiability	en
dc.subject	Stochastic Demand	en
dc.subject	Time Window	en
dc.subject	Travel Time	en
dc.subject	Vehicle Routing	en
dc.subject	Vehicle Routing Problem	en
dc.subject	Working Hours	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.title	Very large scale vehicle routing with time windows and stochastic demand using genetic algorithms with parallel fitness evaluation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/3-540-45492-6_47	en
heal.identifier.secondary	http://dx.doi.org/10.1007/3-540-45492-6_47	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	This paper deals with a real-life vehicle routing problem concerning the distribution of products to customers. A non-homogenous fleet of trucks with limited capacity and allowed travel time is available to satisfy the stochastic multiple product demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs while maximizing customer satisfaction and respecting the constraints concerning the vehicle capacity, the time windows for customer service and the driver working hours per day. A model describing all these requirements has been developed as well as a generic algorithm to solve the problem. High Performance Computing has been used to allow the pursuit for a near-optimal solution in a sensible amount of time, as the parallel chromosome fitness evaluation counterbalances the increased size and complexity of the problem.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	HIGH PERFORMANCE COMPUTING AND NETWORKING, PROCEEDINGS	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/3-540-45492-6_47	en
dc.identifier.isi	ISI:000166853100047	en
dc.identifier.volume	1823	en
dc.identifier.spage	467	en
dc.identifier.epage	476	en