Improved NSGA-III algorithm for solving multi-objective flexible job shop scheduling problem

A workshop scheduling model considering machine resources, spatial resources, and human resources is established for the flexible job shop scheduling problem (FJSP). An improved NSGA-III algorithm is proposed to solve the multi-objective optimization problem that incorporates the makespan, total jobs movement distance, and total workshop payroll. The algorithm enhances the diversity of the population by introducing the Logistic chaotic mapping initialization to the NSGA-III algorithm. Furthermore, a adaptive local search strategy based on the upper confidence bound algorithm (UCB) is employed to enhance the algorithm's search capability. Finally, the effectiveness and practicality of the algorithm are validated through instance verification.