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An efficient local search with noising strategy for Google Machine Reassignment problem

Author

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  • Haris Gavranović

    (International University of Sarajevo)

  • Mirsad Buljubašić

    (Ecole des Mines d’Ales)

Abstract

We present a local search method combined with noising strategy to efficiently solve the Google Machine Reassignment problem (GMRP), proposed at the ROADEF/EURO Challenge 2012 competition. The GMRP is a challenging and novel optimization problem, aimed at maximizing the usage of a set of machines by reallocating processes among those machines in a cost-efficient manner, while respecting a set of technological constraints. The search explores three different neighborhoods. Intensification and diversification of the search is achieved through the noising strategy, sorting the processes and search restarts. The noising is done by simple and suitable change of the objective function. The method is tested on 30 instances proposed by Google and used challenge evaluation. Most of the numerical results obtained here are proven to be optimal, near optimal, or the best known. The presented method was ranked first at ROADEF/EURO Challenge 2012 competition.

Suggested Citation

  • Haris Gavranović & Mirsad Buljubašić, 2016. "An efficient local search with noising strategy for Google Machine Reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 19-31, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1686-3
    DOI: 10.1007/s10479-014-1686-3
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    References listed on IDEAS

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    1. Charon, Irene & Hudry, Olivier, 2001. "The noising methods: A generalization of some metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 86-101, November.
    2. Mutsunori Yagiura & Toshihide Ibaraki & Fred Glover, 2004. "An Ejection Chain Approach for the Generalized Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 16(2), pages 133-151, May.
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    Cited by:

    1. Michel Vasquez & Mirsad Buljubasic & Saïd Hanafi, 2023. "An efficient scenario penalization matheuristic for a stochastic scheduling problem," Journal of Heuristics, Springer, vol. 29(2), pages 383-408, June.

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