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Guided construction search metaheuristics for the capacitated p-median problem with single source constraint

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  • I H Osman

    (American University of Beirut)

  • S Ahmadi

    (De Monfort University)

Abstract

In the capacitated p-median problem with single source constraint, also known as the capacitated clustering problem, a given set of n weighted points is to be partitioned into p clusters such that the total weight of the points in each cluster does not exceed a given cluster capacity. The objective is to find a set of p centres that minimizes the total scatter of points allocated to these clusters. In this paper, a (λ, μ)-interchange neighbourhood based on the concept of λ-interchange of points restricted to μ-adjacent clusters is proposed. Structural properties of centres are identified and exploited to derive special data structures for their efficient evaluations. Different search and selection strategies including the variable neighbourhood search descent with respect to μ-nearest points are investigated. The most efficient strategies are then embedded in a guided construction search metaheuristic framework based either on a periodic local search procedure or a greedy random adaptive search procedure to solve the problem. Computational experience is reported on a standard set of benchmarks. The computational experience demonstrates the competitive performance of the proposed algorithms when compared to the best-known procedures in the literature in terms of solution quality and computational requirement.

Suggested Citation

  • I H Osman & S Ahmadi, 2007. "Guided construction search metaheuristics for the capacitated p-median problem with single source constraint," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 100-114, January.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:1:d:10.1057_palgrave.jors.2602135
    DOI: 10.1057/palgrave.jors.2602135
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    References listed on IDEAS

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    Cited by:

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    2. Kim, Dong-Guen & Kim, Yeong-Dae, 2010. "A branch and bound algorithm for determining locations of long-term care facilities," European Journal of Operational Research, Elsevier, vol. 206(1), pages 168-177, October.
    3. Mai, Feng & Fry, Michael J. & Ohlmann, Jeffrey W., 2018. "Model-based capacitated clustering with posterior regularization," European Journal of Operational Research, Elsevier, vol. 271(2), pages 594-605.
    4. Dong-Guen Kim & Yeong-Dae Kim, 2013. "A Lagrangian heuristic algorithm for a public healthcare facility location problem," Annals of Operations Research, Springer, vol. 206(1), pages 221-240, July.
    5. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    6. Bard, Jonathan F. & Jarrah, Ahmad I., 2009. "Large-scale constrained clustering for rationalizing pickup and delivery operations," Transportation Research Part B: Methodological, Elsevier, vol. 43(5), pages 542-561, June.

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