A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows

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dc.creatorGeiger, Martin Josef
dc.date2008-09-02
dc.date.accessioned2026-07-07T09:59:59Z
dc.date.available2026-07-07T09:59:59Z
dc.descriptionThe article describes an investigation of the effectiveness of genetic algorithms for multi-objective combinatorial optimization (MOCO) by presenting an application for the vehicle routing problem with soft time windows. The work is motivated by the question, if and how the problem structure influences the effectiveness of different configurations of the genetic algorithm. Computational results are presented for different classes of vehicle routing problems, varying in their coverage with time windows, time window size, distribution and number of customers. The results are compared with a simple, but effective local search approach for multi-objective combinatorial optimization problems.
dc.identifierhttps://arxiv.org/abs/0809.0410
dc.identifierhttp://arxiv.org/abs/0809.0410
dc.identifierHabenicht, W. et al. (eds.): Multi-Criteria- und Fuzzy Systeme in Theorie und Praxis-Loesungsansaetze fuer Entscheidungsprobleme mit komplexen Zielsystemen, 2003, ISBN 3-8244-7864-1, pp. 191-207
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/168218
dc.subjectArtificial Intelligence
dc.titleA Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
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