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A Multistart Alternating Tabu Search for Commercial Districting

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10782))

Abstract

In this paper we address a class of commercial districting problems that arises in the context of the distribution of goods. The problem aims at partitioning an area of distribution, which is modeled as an embedded planar graph, into connected components, called districts. Districts are required to be mutually balanced with respect to node attributes, such as number of customers, expected demand, and service cost, and as geometrically-compact as possible, by minimizing their Euclidean diameters. To solve this problem, we propose a multistart algorithm that repeatedly constructs solutions greedily and improves them by two alternating tabu searches, one aiming at achieving feasibility through balancing and the other at maximizing district compactness. Computational experiments confirm the effectiveness of the different components of our method and show that it significantly outperforms the current state of the art, improving known upper bounds in almost all instances.

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Acknowledgments

This research was supported by the Brazilian funding agencies CNPq (grant 420348/2016-6), FAPEMIG (grant TEC-APQ-02694-16) and by Google Research Latin America (grant 25111). We would also like to thank to support of the Fundação de Desenvolvimento Científico e Cultural (FUNDECC/UFLA).

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Authors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Alex Gliesch & Marcus Ritt

  2. Federal University of Lavras, Lavras, Brazil

    Mayron C. O. Moreira

Authors
  1. Alex Gliesch
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  2. Marcus Ritt
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  3. Mayron C. O. Moreira
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Corresponding author

Correspondence to Alex Gliesch .

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Editors and Affiliations

  1. University of Lille, Lille, France

    Arnaud Liefooghe

  2. University of Manchester, Manchester, United Kingdom

    Manuel López-Ibáñez

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Gliesch, A., Ritt, M., Moreira, M.C.O. (2018). A Multistart Alternating Tabu Search for Commercial Districting. In: Liefooghe, A., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018. Lecture Notes in Computer Science(), vol 10782. Springer, Cham. https://doi.org/10.1007/978-3-319-77449-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-77449-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77448-0

  • Online ISBN: 978-3-319-77449-7

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