Parallel Metaheuristics for Workforce Planning
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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.
Key wordsworkforce planning parallel metaheuristics parallel genetic algorithm parallel scatter search parallel hybrid genetic algorithm
Mathematics Subject Classifications (2000)68W15 90C27 90C59
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