基于改进双档案多目标进化算法的 柔性作业车间批量流混排调度.

Aiming at the flexible Job-Shop lot-streaming scheduling problem, based on the single minute exchange of die(SMED), this paper established an intermingling scheduling optimization model with objections of minimizing the makespan and the total number of sub-lots, considering the flexibility of sublots splitting and sublots intermingling, automatic changeover and material transportation. Then it proposed an improved two-archive based multi-objective evolutionary algorithm to optimize the objective function. This algorithm adopted the framework of evolutionary algorithm. Based on the framework of evolutionary algorithm, it designed a two-archive based on hypervolume indicator and improved Pareto dominance to balance the convergence and diversity of the population. And according to the characteristics of lot-streaming intermingling problems, it proposed the forward/backward decoding and sub-lot splitting left-shift strategies in the decoding stage. In the stages of neighborhood exploration and global search, it designed adaptive evolution operators for lot splitting and sub-lot intermingling schemes respectively to improve the global search and local search capabilities of the algorithm. Based on different scale examples, it tested the performance of the proposed algorithm and the classical multi-objective algorithms. The experimental results show that the algorithm has obvious advantages in convergence and diversity. [ABSTRACT FROM AUTHOR]