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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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
Key words
- workforce planning
- parallel metaheuristics
- parallel genetic algorithm
- parallel scatter search
- parallel hybrid genetic algorithm