Abstract: Aiming at the problem that when solving the flexible job shop scheduling problem (FJSP), the traditional swarm intelligence optimization algorithm has some disadvantages, such as insufficient optimization ability and easy to fall into local optimum, taking minimizing the maximum completion time as targets, firefly algorithm (FA) is applied to solve flexible job shop scheduling problem (FJSP), and an improved discrete firefly algorithm (DFA) is proposed. Firstly, the relationship between the FA continuous optimization problem and the FJSP discrete optimization problem is established through two-stage coding. Secondly, a population initialization method is designed to ensure the quality and diversity of initial solutions. Then, an improved discrete firefly optimization algorithm is proposed and a local search algorithm is introduced to enhance the global search ability and local search ability of the algorithm. Finally, the standard example is simulated and the validity of DFA algorithm for FJSP is verified. Through simulation comparison with genetic algorithm and particle swarm optimization algorithm, the superiority of DFA in solving FJSP is verified.

Key words: flexible job shop scheduling problem(FJSP), maximum completion time, discrete firefly algorithm, two-stage coding