Workforce planning with parallel algorithms

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 that requires modern techniques to be solved adequately. In this work, we describe the development of two parallel metaheuristic methods, a parallel genetic algorithm and a parallel scatter search, 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