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Fast machine reassignment


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.

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The authors wish to thank the two anonymous reviewers for fruitful suggestions which help improve a previous version of this paper.

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Correspondence to Franck Butelle.

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Butelle, F., Alfandari, L., Coti, C. et al. Fast machine reassignment. Ann Oper Res 242, 133–160 (2016).

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  • Generalized Assignment
  • Adaptive Variable Neighborhood Search
  • Simulated Annealing
  • Hyper-Heuristic
  • Cooperative Parallel Search