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Dynamic Vehicle Routing: A Memetic Ant Colony Optimization Approach

  • Chapter
Automated Scheduling and Planning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 505))

Abstract

Over the years, several variations of the dynamic vehicle routing problem (DVRP) have been considered due to its similarities with many real-world applications. Several methods have been applied to address DVRPs, in which ant colony optimization (ACO) has shown promising results due to its adaptation capabilities. In this chapter, we generate another variation of the DVRP with traffic factor and propose a memetic algorithm based on the ACO framework to address it. Multiple local search operators are used to improve the exploitation capacity and a diversity scheme based on random immigrants is used to improve the exploration capacity of the algorithm. The proposed memetic ACO algorithm is applied on different test cases of the DVRP with traffic factors and is compared with other peer ACO algorithms. The experimental results show that the proposed memetic ACO algorithm shows promising results.

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Authors and Affiliations

  1. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, U.K.

    Michalis Mavrovouniotis & Shengxiang Yang

Authors
  1. Michalis Mavrovouniotis
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  2. Shengxiang Yang
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Correspondence to Michalis Mavrovouniotis .

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Editors and Affiliations

  1. , Elektrik-Elektronik Fakultesi, Istanbul Teknik Universitesi, Istanbul, 34469, Turkey

    A. Sima Uyar

  2. , School of Computer Science, University of Nottingham, Wollaton Road, Nottingham, NG8 1BB, United Kingdom

    Ender Ozcan

  3. , Centre for Emergent Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, United Kingdom

    Neil Urquhart

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Mavrovouniotis, M., Yang, S. (2013). Dynamic Vehicle Routing: A Memetic Ant Colony Optimization Approach. In: Uyar, A., Ozcan, E., Urquhart, N. (eds) Automated Scheduling and Planning. Studies in Computational Intelligence, vol 505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39304-4_11

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  • DOI: https://doi.org/10.1007/978-3-642-39304-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39303-7

  • Online ISBN: 978-3-642-39304-4

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