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Articles (2016), Annals of Operations Research, 242 (1), pp. 130-160

Fast Machine Reassignment

BUTELLE F., ALFANDARI Laurent , COTI C., FINTA L., LÉTOCART L., PLATEAU G.

This paper proposes a new method for solving the Machine Reassignment Problem in a very short computational time. The problem has been proposed by Google as subject of the Challenge ROADEF/EURO 2012. The Machine Reassignment Problem consists in looking for a reassignment of processes to machines in order to minimize a complex objective function, subject to a rich set of constraints including multidimensional resource, conflict and dependency constraints. In this study, a cooperative search approach is presented for machine reassignment. This approach uses two components: Adaptive Variable Neighbourhood Search and Simulated Annealing based Hyper-Heuristic, running in parallel on two threads and exchanging solutions. Both algorithms employ a rich set of heuristics and a learning mechanism to select the best neighborhood/move type during the search process. The cooperation mechanism acts as a multiple restart which gets triggered whenever a new better solution is achieved by a thread and then shared with the other thread. Computational results on the Challenge instances as well as instances of a Generalized Assignment-like problem are given to show the relevance of the chosen methods and the high benefits of cooperation. Lien vers l'article

BUTELLE, F., ALFANDARI, L., COTI, C., FINTA, L., LÉTOCART, L. and PLATEAU, G. (2016). Fast Machine Reassignment. Annals of Operations Research, 242(1), pp. 130-160.

Mots clés : #Generalized-Assignment, #Adaptive-Variable-Neighborhood-Search, #Simulated-Annealing, #Hyper, #Heuristic, #Cooperative-Parallel-Search