A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm

Dual-resource constrained flexible job shop scheduling problem has become a hot research field in recent years. However, few studies have considered workers' boredom sensations when allocating resources and scheduling tasks, leading to inappropriate task allocation and negative effects such as efficiency reductions and absenteeism. Therefore, a new boredom-aware dual-resource constrained flexible job shop scheduling problem is investigated in this study, which considers the increase in workers' boredom caused by repetitive job assignments and constructs an efficiency function to characterize the impact of workers' boredom. For this problem, a bi-level lexicographic model, which takes the effective completion of all manufacturing tasks as the primary optimization objective and higher worker job satisfaction as the secondary optimization objective, is established. A two-stage multi-objective particle swarm optimization algorithm with a three-dimensional representation scheme is presented to solve this model. In this algorithm, a new position-updating mechanism and a local search strategy are devised to effectively evolve the solution, and a decomposition strategy with a self-adaptive neighborhood size and a boundary exploration mechanism are embedded to obtain a uniformly distributed Pareto front. Experimental results confirm the superiority of the presented model and algorithm.