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A multiple ant colony system with random variable neighborhood descent for the dynamic vehicle routing problem with time windows

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Abstract

This paper proposes a framework to solve the dynamic vehicle routing problem with time windows. This problem involves determining the minimum cost routes of a homogeneous fleet for meeting the demand for a set of customers within time windows. In addition, new customers can be assigned to vehicles during the execution of the routes. A framework is based on two phases: a priori where the routes are obtained for the known customers using static routing, and a posteriori where routes are re-optimized repeatedly during the planning horizon either continuously or periodically. The framework was validated using seven algorithm variants based on insertion heuristic, ant colony optimization, variable neighborhood descent, and random variable neighborhood descent, which were adapted to solve a posteriori phase. The best algorithm is a hybrid version that combines an improved version of the multiple ant colony systems with a random variable neighborhood descent. Computational results show that most of the algorithms are competitive regarding the state of the art with the best results in the objective of minimizing the number of unserved customers.

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Acknowledgements

This work was supported by CNPq—National Council for Scientific and Technological Development under Grant No. 140015/2011-6, CAPES—Brazilian Federal Agency for Support and Evaluation of Graduate Education under Grant No. 001, and FAPERJ—Foundation for the Research Support of the State of Rio de Janeiro under Grant No. E-26/202.175/2015.

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Authors and Affiliations

  1. Department of Fortification and Construction Engineering, Military Institute of Engineering, Praça General Tibúrcio, 80, Urca, Rio de Janeiro, RJ, Brazil

    Orivalde Soares da Silva Júnior

  2. Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22451-900, Brazil

    José Eugênio Leal

  3. Department for Production and Operations Management, University of Graz, Universitätsstrasse 15/E3, 8010, Graz, Austria

    Marc Reimann

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  1. Orivalde Soares da Silva Júnior
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  2. José Eugênio Leal
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  3. Marc Reimann
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Correspondence to Orivalde Soares da Silva Júnior.

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Benchmark instances and Tables 5 to 9 are available at https://github.com/orivalde/dvrptw (DOCX 54 kb)

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da Silva Júnior, O.S., Leal, J.E. & Reimann, M. A multiple ant colony system with random variable neighborhood descent for the dynamic vehicle routing problem with time windows. Soft Comput 25, 2935–2948 (2021). https://doi.org/10.1007/s00500-020-05350-4

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