A Multiform Evolutionary Framework for Multiobjective Fuzzy Flexible Job Shop Scheduling: A Preliminary Study
Shaojin Geng, Chen Ni, Weian Guo, Dongyang Li, Dongqing Xie, Qin Zhang, Lei Wang, Qidi Wu, Wuzhao LiABSTRACT
The fuzzy flexible job shop scheduling problem (FFJSS) is a challenging NP‐hard problem due to the combined effects of operation sequencing, flexible machine assignment, and fuzzy processing times. Existing multi‐objective evolutionary algorithms mainly emphasize convergence and diversity in the objective space, while often overlooking the structure in the decision space induced by machine flexibility. This paper presents a multiform evolutionary framework for multi‐objective FFJSS by co‐evolving the original task and an auxiliary task with restricted machine choices. The auxiliary task reduces decision‐space complexity and encourages stable scheduling structures, which are implicitly transferred to guide the search on the original task. Experimental results on benchmark instances demonstrate improved convergence and solution quality over representative algorithms. Further analysis shows that high‐quality nondominated solutions tend to assign most operations to the most efficient machines, supporting the effectiveness of the proposed multiform design.