A study of the multi-objective flexible job-shop scheduling model considering human factors

This research introduces a comprehensive multi-objective mathematical model for the flexible job-shop scheduling problem (FJSP), designed to optimise human-machine efficiency and worker health. The model aims to minimise the total production cost while considering the makespan, delivery delay, labour cost, and ergonomic risks. In particular, the assessment of ergonomic risk is performed using the OCRA method, ensuring the health and safety of workers. Due to the nonlinear nature of the model, this study proposes a custom developed genetic algorithm (GA) and artificial bee colony algorithm (ABCA) to find solutions. To verify the accuracy of these two algorithms, we also compared them with an enumeration method in small-scale production scenarios. Numerical experiments validate the effectiveness of the model in balancing production efficiency with worker welfare, providing a holistic and human-centred approach to addressing scheduling challenges in intelligent manufacturing systems.