Guided shuffled frog-leaping algorithm for flexible job shop scheduling problem with variable sublots and overlapping in operations
Yunfan Yang, Song Yuchuan, Weifei Guo, Lei Qi, Aihong Sun, Lianghua Fan
Abstract
In this study, a guided shuffled frog-leaping algorithm (GSFLA) is proposed to minimise the makespan of the flexible job shop scheduling problem with variable sublots and overlapping in operations (FJSP-VSOO). In order to express the solution space more efficiently and completely for the co-optimization of batching and scheduling problems, an operation-level batching encoding considering sub-batches splitting is designed. Considering the characteristics of overlapping in operations, an active decoding method based on the unit assignment and right-shift operation is developed. To ensure the quality and diversity of the initial population, a hybrid initialization method based on dynamic load balancing is proposed. In addition, to balance the global and local search capabilities of the GSFLA, a sublot disturbance based local search algorithm is embedded in the process of the priority guide memeplex evolution, which can adaptively adjust the search tendency of the algorithm through the priority guide operation. Finally, the effectiveness of the proposed methods is verified in extensive experiments based on instances of FJSP with lot streaming (LS) on different scales. Moreover, compared with some advanced algorithms, the results show that the GSFLA has superiority in terms of optimization ability and solution efficiency.