A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop

• Published in: Knowledge-based systems Vol. 194; p. 105536
• Main Authors: Zheng, Jie, Wang, Ling, Wang, Jing-jing
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 22.04.2020; Elsevier Science Ltd
• Subjects: Algorithms; Cooperative coevolution algorithm; Design of experiments; Entropy (Information theory); Estimation of distribution algorithm; Exploitation; Fuzzy processing times and due dates; Iterated greedy search; Job shops; Multi-objective fuzzy distributed hybrid flow shop; Performance enhancement; Robustness; Robustness (mathematics); Searching; Solution space; Statistical analysis; Taguchi methods; Cooperative coevolution algorithm; Robustness; Multi-objective fuzzy distributed hybrid flow shop; Fuzzy processing times and due dates; Iterated greedy search; Estimation of distribution algorithm
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2020.105536

With consideration of uncertainty in the distributed manufacturing systems, this paper addresses a multi-objective fuzzy distributed hybrid flow shop scheduling problem with fuzzy processing times and fuzzy due dates. To optimize the fuzzy total tardiness and robustness simultaneously, a cooperative coevolution algorithm with problem-specific strategies is proposed by reasonably combining the estimation of distribution algorithm (EDA) and the iterated greedy (IG) search. In the EDA-mode search, a problem-specific probability model is established to reduce the solution space and a sample mechanism is proposed to generate new individuals. To enhance exploitation, a specific local search is designed to improve performance of non-dominated solutions. Moreover, destruction and reconstruction methods in the IG-mode search are employed for further exploiting better solutions. To balance exploration and exploitation capabilities, a cooperation scheme for mode switching is designed based on the information entropy and the diversity of elite solutions. The effect of the key parameters on the performances of the proposed algorithm is investigated by Taguchi design of experiment method. Comparative results and statistical analysis demonstrate the effectiveness of the proposed algorithm in solving the problem. •Multi-objective fuzzy distributed hybrid flow shop scheduling is addressed.•Estimation of distribution algorithm and iterated greedy are cooperated.•Probability model and sample mechanism are designed.•Problem-specific local search is designed.•Information entropy and diversity are used to design cooperation scheme.