An enhanced teaching–learning-based optimization for the flexible job shop scheduling problem considering worker behaviours

In actual workshop scheduling, processing time is influenced by worker behaviours, such as learning, forgetting, fatigue and recovery. However, in the traditional workshop scheduling considering workers, these behaviours are ignored, resulting in inaccurate scheduling schemes. Therefore, this paper investigates a multi-objective flexible job shop scheduling problem considering worker behaviours (WBFJSP) with makespan, total energy consumption and total cost. First, a multi-objective optimization model is established according to the characteristics of WBFJSP. In this model, the learning–forgetting–fatigue–recovery model about processing time is established. Then, an enhanced teaching–learning-based optimization (ETLBO) is proposed. In order to enhance the search ability of the algorithm, each component of ETLBO is designed separately. In the initialization phase, four heuristics are proposed to enhance the diversity of the initial population. In teaching stage, a dynamically adjusted teaching method is proposed to achieve exploration and exploitation. In teacher self-study stage, three local search strategies are proposed to improve the quality of the solution. Finally, the feasibility and the effectiveness of the proposed ETLBO are verified by extensive experiments.