An enhanced memetic algorithm with hierarchical heuristic neighborhood search for type-2 green fuzzy flexible job shop scheduling

Abstract

The green flexible job shop has received increasing attention due to the development of modern industry and the improvement of environmental protection awareness. Meanwhile, in customized manufacturing, the processing time cannot be exactly predicted. Therefore, it can be predicted by the type-2 fuzzy system, which can effectively characterize the uncertainty of processing time. This work aims to solve the green type-2 fuzzy flexible job shop problem (T2FJSP), considering minimizing total energy consumption (TEC) and the makespan simultaneously. Memetic algorithms are used for the T2FJSP, however, most of them do not allocate local search opportunities based on the quality of solutions, resulting in the waste of computing resources. In this paper, an enhanced memetic algorithm with hierarchical heuristic neighborhood search (EMAH) is proposed to overcome the shortcoming. EMAH has the following features: (i) a cooperative heuristic initialization is designed by combining six heuristics to generate high-quality solutions; (ii) a hierarchical heuristic neighborhood search strategy is proposed to better enhance the exploitation by allocating computing resources based on the quality of the solutions; and (iii) an energy-saving strategy is designed to decrease the makespan and TEC. The effectiveness of EMAH is verified by comparing it with the state-of-the-art algorithms in a T2FJSP benchmark. The results indicate the superiority of EMAH in solving the T2FJSP.