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Parallel Metaheuristics for Workforce Planning

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Journal of Mathematical Modelling and Algorithms

Abstract

Workforce planning is an important activity that enables organizations to determine the workforce needed for continued success. A workforce planning problem is a very complex task requiring modern techniques to be solved adequately. In this work, we describe the development of three parallel metaheuristic methods, a parallel genetic algorithm, a parallel scatter search, and a parallel hybrid genetic algorithm, which can find high-quality solutions to 20 different problem instances. Our experiments show that parallel versions do not only allow to reduce the execution time but they also improve the solution quality.

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Authors and Affiliations

  1. Department of Languages and Computational Sciences, University of Málaga, 29071, Málaga, Spain

    Enrique Alba, Gabriel Luque & Francisco Luna

Authors
  1. Enrique Alba
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  2. Gabriel Luque
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  3. Francisco Luna
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Corresponding author

Correspondence to Enrique Alba.

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Alba, E., Luque, G. & Luna, F. Parallel Metaheuristics for Workforce Planning. J Math Model Algor 6, 509–528 (2007). https://doi.org/10.1007/s10852-007-9058-5

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  • DOI: https://doi.org/10.1007/s10852-007-9058-5

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