Balancing Problem of Stochastic Large-Scale U-Type Assembly Lines Using a Modified Evolutionary Algorithm

• Published in: IEEE access Vol. 6; pp. 78414 - 78424
• Main Authors: Zhang, Honghao, Zhang, Chaoyong, Peng, Yong, Wang, Danqi, Tian, Guangdong, Liu, Xu, Peng, Yuexiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ant colony optimization; Assembly lines; Balancing; data analysis; evolutionary algorithm; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Heuristic algorithms; large-scale; Mathematical analysis; Mathematical model; Mathematical models; Simulated annealing; Stochastic processes; stochastic properties; Straight lines; Task analysis; U-type assembly line; Workstations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2885030

U-type assembly lines have become a mainstream mode in manufacturing because of the higher flexibility and productivity compared with straight lines. Since the balancing problem of a large-scale U-type assembly line is known to be NP-hard, effective mathematical model and evolutionary algorithm are needed to solve this problem. This paper reviews the research status of the related literature in recent years and presents a hybrid evolutionary algorithm, namely, modified ant colony optimization inspired by the process of simulated annealing, to reduce the possibility of being trapped in a local optimum for the balancing problem of stochastic large-scale U-type assembly line. A modified mathematical model for this balancing problem considering stochastic properties is formulated. Furthermore, comparisons with genetic algorithm and imperialist competitive algorithm are conducted to evaluate this proposed method. The results indicate that this proposed algorithm outperforms prior methods in this balancing problem.