Abstract: This paper focuses on the flexible job shop scheduling problem and proposes an improved gray wolf optimization (IGWO) algorithm, with the goal of minimizing the maximum completion time. A two-phase coding method is used to construct the relationship between the individual locations and the scheduling scheme. The initial population method based on the heuristic rule is used to improve the quality of initial solution. In order to balance global search and local search, a hyperbolic-tangent-function-based non-linear convergence factor formula is proposed, in the individual update stage of the algorithm, a weighting method based on fitness value is proposed, the variable neighborhood search algorithm is embedded into the decision-making layer of the algorithm. Simulation results show that the algorithm is effective in solving the flexible job shop scheduling problem.

Key words: flexible job shop scheduling, grey wolf optimization, variable neighborhood search algorithm, nonlinear convergence factor