Optimizing the usage of resources is an important topic in the development of technologies and computational services. The google machine reassignment problem is an NP-hard problem that is related to this crucial situ...
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Optimizing the usage of resources is an important topic in the development of technologies and computational services. The google machine reassignment problem is an NP-hard problem that is related to this crucial situation, based on the assignation of a set of processes into a set of machines trying to reduce several costs. This problem was proposed for the 2012 ROADEF/EURO challenge and since its introduction, many approaches have been proposed in order to reach better quality solutions or improve the execution time of the existing techniques. In this work, we review a significant number of recently proposed approaches. Due to the number of published papers, it is difficult to ascertain the level of current research in this area. In order to provide a useful guide to new interested researchers, we include up-to-date best-known results for benchmark instances, an analysis of the design of each technique and details of the experimental setup. We also present a classification and a taxonomy of the reviewed approaches based on the design of these techniques, considering their main components and the structure of the search strategies.
It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm ...
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ISBN:
(纸本)9783319516912;9783319516905
It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm is used in this study as it is parameter free. The search problem used is the google machine reassignment problem (GMRP). GMRP is a recent real world problem proposed at ROADEF/EURO challenge 2012 competition. It consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. In this paper, the effectiveness of three neighbourhood structures and their combinations are evaluated on GMRP instances, which are very diverse in terms of number of processes, resources and machines. The results show that neighbourhood structure does have impact on search performance. A combined neighbourhood structures with SD can achieve results better than SD with single neighbourhood structure.
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