A reinforcement learning methodology for a human resource planning problem considering knowledge-based promotion

This paper addresses a combined problem of human resource planning (HRP) and production-inventory control for a high-tech industry, wherein the human resource plays a critical role. The main characteristics of this resource are the levels of “knowledge” and the learning process. The learning occurs during the production process in which a worker can promote to the upper knowledge level. Workers in upper levels have more productivity in the production. The objective is to maximize the expected profit by deciding on the optimal numbers of workers in various knowledge levels to fulfill both production and training requirement. As taking an action affects next periods' decisions, the main problem is to find the optimal hiring policy of non-skilled workers in long-time horizon. Thus, we develop a reinforcement learning (RL) model to obtain the optimal decision for hiring workers under the demand uncertainty. The proposed interval-based policy of our RL model, in which for each state there are multiple choices, makes it more flexible. We also embed some managerial issues such as layoff and overtime-working hours into the model. To evaluate the proposed methodology, stochastic dynamic programming (SDP) and a conservative method implemented in a real case study are used. We study all these methods in terms of four criteria: average obtained profit, average obtained cost, the number of new-hired workers, and the standard deviation of hiring policies. The numerical results confirm that our developed method end up with satisfactory results compared to two other approaches.