Fragnière et al. [21] proposed extensions to a popular aggregate planning model using multistage stochastic programming in which the demand and the production capacity obtained by qualified and non-qualified employees were stochastic variables. Gans and Zhou [22] addressed employee learning and turnover in a staffing problem to meet forecasted service requirements. They considered learning lead-time, stochastic turnovers, and learning rates to find the optimal hiring policy. Georgiou and Tsantas [23] also suggested a non-homogeneous Markov chain model to optimize the operations of both training new-hired workers and improving the knowledge of existing ones. The main focus of these papers was learning an issue in which the knowledge levels and promotion between them were not clearly explained.