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

Haris Gavranović () and Mirsad Buljubašić ()
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Haris Gavranović: International University of Sarajevo
Mirsad Buljubašić: Ecole des Mines d’Ales

Annals of Operations Research, 2016, vol. 242, issue 1, No 2, 19-31

Abstract: 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.

Keywords: Local search; Noising; Machine reassignment (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10479-014-1686-3

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