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Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem

Author

Listed:
  • Z P Fan

    (Northeastern University)

  • Y Chen

    (Shanghai University of Finance & Economics)

  • J Ma

    (City University of Hong Kong)

  • S Zeng

    (University of Arizona)

Abstract

Corrections to: Journal of the Operational Research Society (2010). doi: 10.1057/jors.2009.168 ; published online 6 January 2010 The maximally diverse grouping problem (MDGP) is a NP-complete problem. For such NP-complete problems, heuristics play a major role in searching for solutions. Most of the heuristics for MDGP focus on the equal group-size situation. In this paper, we develop a genetic algorithm (GA)-based hybrid heuristic to solve this problem considering not only the equal group-size situation but also the different group-size situation. The performance of the algorithm is compared with the established Lotfi–Cerveny–Weitz algorithm and the non-hybrid GA. Computational experience indicates that the proposed GA-based hybrid algorithm is a good tool for solving MDGP. Moreover, it can be easily modified to solve other equivalent problems.

Suggested Citation

  • Z P Fan & Y Chen & J Ma & S Zeng, 2011. "Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1423-1430, July.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:7:d:10.1057_jors.2010.92
    DOI: 10.1057/jors.2010.92
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    References listed on IDEAS

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    Cited by:

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    7. Ríos-Mercado, Roger Z. & Bard, Jonathan F., 2019. "An exact algorithm for designing optimal districts in the collection of waste electric and electronic equipment through an improved reformulation," European Journal of Operational Research, Elsevier, vol. 276(1), pages 259-271.

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