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

Honghao Zhang | Zendy; Chaoyong Zhang | Zendy; Yong Peng | Zendy; Danqi Wang | Zendy; Guangdong Tian | Zendy; Xu Liu | Zendy; Yuexiang Peng | Zendy

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

Author(s) -

Honghao Zhang,

Chaoyong Zhang,

Yong Peng,

Danqi Wang,

Guangdong Tian,

Xu Liu,

Yuexiang Peng

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2885030

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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