Parallel Metaheuristics for Workforce Planning
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.