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

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Book cover Multi-Criteria- und Fuzzy-Systeme in Theorie und Praxis

Abstract

The 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.

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Authors and Affiliations

  1. Production and Operations Management Department, University of Hohenheim, 70593, Stuttgart, Germany

    Martin Geiger

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  1. Martin Geiger
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Walter Habenicht Beate Scheubrein Ralph Scheubrein

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© 2003 Deutscher Universitäts-Verlag/GWV Fachverlage GmbH, Wiesbaden

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Geiger, M. (2003). A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows. In: Habenicht, W., Scheubrein, B., Scheubrein, R. (eds) Multi-Criteria- und Fuzzy-Systeme in Theorie und Praxis. Deutscher Universitätsverlag. https://doi.org/10.1007/978-3-322-81539-2_10

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  • DOI: https://doi.org/10.1007/978-3-322-81539-2_10

  • Publisher Name: Deutscher Universitätsverlag

  • Print ISBN: 978-3-8244-7864-4

  • Online ISBN: 978-3-322-81539-2

  • eBook Packages: Springer Book Archive

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