Aiming at the flexible job shop lot-streaming scheduling problem, based on the single minute exchange of die (SMED) , establish an intermingling scheduling optimization model with objections of minimizing the makespan and the total number of sub-lots, where the flexibility of sublots splitting and sublots intermingling, automatic changeover and material transportation are considered. Then propose an improved two-archive based multi-objective evolutionary algorithm to optimize the objective function. This algorithm adopts the framework of evolutionary algorithm. Based on the framework of evolutionary algorithm, design 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, propose the forward/backward decoding and sub-lot splitting left-shift strategies in the decoding stage. In the stages of neighborhood exploration and global search, design 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, test the performance of the proposed algorithm and the classical multi-objective algorithm. The experimental results show that the algorithm has obvious advantages in convergence and diversity.

[2024-02-02] Accepted Paper /doc/2023.09.0499/v1