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