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

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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


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