Hybrid Memetic Algorithm to Solve Multiobjective Distributed Fuzzy Flexible Job Shop Scheduling Problem with Transfer

Most studies on distributed flexible job shop scheduling problem (DFJSP) assume that both processing time and transmission are crisp values. However, due to the complexity of factory environment, information is uncertain. Therefore, we consider uncertainty for first propose a multiobjective fuzzy with transfer (MO-DFFJSPT). To solve MO-DFFJSPT, hybrid decomposition variable neighborhood memetic algorithm (HDVMA) proposed objectives minimizing makespan, maximum load, total workload. In HDVMA, well-designed encoding/decoding method four initialization rules used generate initial population, several effective evolutionary operators designed update populations. Additionally, weight vector introduced design high quality individual selection acceptance criteria. Then, three excellent local search (VNS) enhance its exploitation capability. Finally, Taguchi experiment adjust important parameters. Fifteen benchmarks constructed, HDVMA compared other famous algorithms metrics. The experimental results show superior in terms convergence uniformity non-dominated solution set distribution.