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Constraint-based large neighborhood search for machine reassignment

Author

Listed:
  • Felix Brandt

    (Research Center for Information Technology, Logistics and Supply Chain Optimization)

  • Jochen Speck

    (Karlsruhe Institute of Technology)

  • Markus Völker

    (Karlsruhe Institute of Technology)

Abstract

This paper addresses a process-to-machine reassignment problem arising in cloud computing environments. The problem formulation has been posed as the ROADEF/EURO challenge 2012. Our presented approach is basically a large neighborhood search that iteratively improves a given solution. In each iteration only a subset of processes is considered for reassignment and the new assignments are evaluated by a constraint program. In this paper we present our general solution approach. Furthermore, we evaluate different process selection strategies and other optimization means to improve the performance on larger instances. In addition, we present a simple way to compute tight lower bounds of the necessary costs.

Suggested Citation

  • Felix Brandt & Jochen Speck & Markus Völker, 2016. "Constraint-based large neighborhood search for machine reassignment," Annals of Operations Research, Springer, vol. 242(1), pages 63-91, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1772-6
    DOI: 10.1007/s10479-014-1772-6
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    Cited by:

    1. Takfarinas Saber & Xavier Gandibleux & Michael O’Neill & Liam Murphy & Anthony Ventresque, 2020. "A comparative study of multi-objective machine reassignment algorithms for data centres," Journal of Heuristics, Springer, vol. 26(1), pages 119-150, February.

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