An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

In order to solve the flexible job shop scheduling problem with variable batches, we propose an improved multi-objective optimization algorithm, which combines idea of inverse scheduling. First, a batches model is formulated. Second, batch algorithm in size adjusted by dynamic feedback adjusting method. Moreover, increase diversity population, two methods are developed. One threshold control neighborhood updating, and other clustering update population. Finally, group experiments carried out. The results show that can ensure Pareto solutions effectively, has effective performance solving batches.