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Improving simulated annealing with variable neighborhood search to solve the resource-constrained scheduling problem

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Abstract

The purpose of this paper is to improve the simulated annealing method with a variable neighborhood search to solve the resource-constrained scheduling problem. We also compare numerically this method with other neighborhood search (local search) techniques: threshold accepting methods and tabu search. Furthermore, we combine these techniques with multistart diversification strategies and with the variable neighborhood search technique. A thorough numerical study is completed to set the parameters of the different methods and to compare the quality of the solutions that they generate. The numerical results indicate that the simulated annealing method improved with a variable neighborhood search technique is indeed the best solution method.

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Authors and Affiliations

  1. University of Montreal, CP 6128, Succ Centre-Ville, Montreal, PQ, H3C 3J7, Canada

    Véronique Bouffard & Jacques A. Ferland

Authors
  1. Véronique Bouffard
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  2. Jacques A. Ferland
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Corresponding author

Correspondence to Jacques A. Ferland.

Additional information

This research was supported by NSERC grant (OGP 0008312) the first author received a FCAR fellowship during her M.Sc. studies.

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Bouffard, V., Ferland, J.A. Improving simulated annealing with variable neighborhood search to solve the resource-constrained scheduling problem. J Sched 10, 375–386 (2007). https://doi.org/10.1007/s10951-007-0043-7

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