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Fast machine reassignment

Abstract

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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Notes

  1. 1.

    http://challenge.roadef.org/2012.

  2. 2.

    FMR is open source and is distributed under GPL, see http://www.lipn.fr/~butelle/s26.tgz.

  3. 3.

    For more detailed results and information see http://challenge.roadef.org/2012/en/results.php.

  4. 4.

    See http://www-or.amp.i.kyoto-u.ac.jp/~yagiura/mrgap.

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Acknowledgments

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). https://doi.org/10.1007/s10479-015-2082-3

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Keywords

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