An improved discrete group teaching optimization algorithm for multi-objective flexible job shop scheduling problem

Abstract This paper proposes a multi-objective discrete group teaching optimization algorithm (IGTOA) to solve the flexible job shop scheduling problem (FJSP) considering the minimization of maximum completion time, total workload of all machines, and maximum machine workload. In the proposed algorithm, in order to adapt the discrete nature of FJSP, a special encoding method is adopted to represent solutions. Additionally, two discretized update schemes are introduced to enhance the algorithm’s exploration ability. Moreover, based on the critical path concept, the variable neighborhood search (VNS) and insert operation are employed for local search. Finally, a dual-mode environmental selection using non-dominated ranking and crowding distance is designed to maintain the population diversity and convergence. The performance of IGTOA is verified by three well-known benchmark comparisons with several state-of-the-art algorithms. Experimental results indicate that the proposed algorithm is effective and efficient for FJSP.