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Solving the capacitated clustering problem with variable neighborhood search

Author

Listed:
  • Jack Brimberg

    (Royal Military College of Canada)

  • Nenad Mladenović

    (Mathematical Institute SANU)

  • Raca Todosijević

    (Mathematical Institute SANU)

  • Dragan Urošević

    (Mathematical Institute SANU)

Abstract

Variable neighborhood search (VNS) is a proven heuristic framework for finding good solutions to combinatorial and global optimization problems. In this paper two VNS-based heuristics are proposed for solving the capacitated clustering problem. The first follows a standard VNS approach, and the second a skewed VNS that allows moves to inferior solutions. The performance of the two heuristics is assessed on benchmark instances from the literature. We also compare their performance against a recently published iterated VNS procedure. All VNS procedures outperform the state-of-the-art, but the Skewed VNS is best overall. This would suggest that using acceptance criteria before allowing moves to inferior solutions in Skewed VNS is preferable to the random shaking approach that is used in Iterated VNS to move to new regions of the solution space.

Suggested Citation

  • Jack Brimberg & Nenad Mladenović & Raca Todosijević & Dragan Urošević, 2019. "Solving the capacitated clustering problem with variable neighborhood search," Annals of Operations Research, Springer, vol. 272(1), pages 289-321, January.
    • Handle: RePEc:spr:annopr:v:272:y:2019:i:1:d:10.1007_s10479-017-2601-5
    DOI: 10.1007/s10479-017-2601-5
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    References listed on IDEAS

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    1. Anna Martínez-Gavara & Vicente Campos & Micael Gallego & Manuel Laguna & Rafael Martí, 2015. "Tabu search and GRASP for the capacitated clustering problem," Computational Optimization and Applications, Springer, vol. 62(2), pages 589-607, November.
    2. Duarte, Abraham & Marti, Rafael, 2007. "Tabu search and GRASP for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 178(1), pages 71-84, April.
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    Cited by:

    1. Lili Wang & Min Li & Guanbin Kong & Haiwen Xu, 2024. "Joint decision-making for divisional seru scheduling and worker assignment considering process sequence constraints," Annals of Operations Research, Springer, vol. 338(2), pages 1157-1185, July.
    2. Martí, Rafael & Martínez-Gavara, Anna & Pérez-Peló, Sergio & Sánchez-Oro, Jesús, 2022. "A review on discrete diversity and dispersion maximization from an OR perspective," European Journal of Operational Research, Elsevier, vol. 299(3), pages 795-813.
    3. Vesna Radonjić Ɖogatović & Marko Ɖogatović & Milorad Stanojević & Nenad Mladenović, 2020. "Revenue maximization of Internet of things provider using variable neighbourhood search," Journal of Global Optimization, Springer, vol. 78(2), pages 375-396, October.
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    5. Zhiyuan Yuan & Jie Gao, 2022. "Dynamic Uncertainty Study of Multi-Center Location and Route Optimization for Medicine Logistics Company," Mathematics, MDPI, vol. 10(6), pages 1-15, March.

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