GA based algorithm for staff scheduling considering learning-forgetting effect

Learning-forgetting effect reflects productivity changes related to the increment of experience cumulated by previous production volume, which is a critical issue for production efficiency analysis. This paper proposes a GA based staff scheduling methodology for solving personnel efficiency problem caused by learning-forgetting effect, aiming at obtaining scheduling solution in both parallel and serial systems. Individual capability is modeled considering his/her initial and maximum capabilities, as well as learning/forgetting rates on corresponding tasks. Two experiments are designed in scenarios for parallel and serial production lines and strategies of staff scheduling are generated considering learning-forgetting phenomenon. Simulation results illustrate the effectiveness of the proposed methodology.