Solving Multi-Objective Two-Sided Assembly Line Balancing Problems by Harmony Search Algorithm Based on Pareto Entropy

• Main Authors: Xiaojun Zheng, Shiduo Ning, Hao Sun, Jiang Zhong, Xiaoying Tong
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Harmony search algorithm; multi-objective optimization; Pareto entropy; two-sided assembly line balancing
• Online Access: https://ieeexplore.ieee.org/document/9524913/

Two-sided assembly lines are designed to produce large and complex products, where workers can perform on both sides at the same time. This paper establishes a mathematical model for the multi-objective two-sided assembly line balancing problems with additional constraints (MOATALBP). The model considers both workers skills and the balance of the assembly line, aiming to maximize efficiency and minimize workers cost and smoothness index. A harmony search algorithm (HS) based on Pareto entropy (PE-MHS) is proposed to solve MOATALBP. The difference entropy of Pareto solutions is employed to adjust the algorithm parameters to enhance the optimization ability of PE-MHS. Moreover, a fine-tuning operation combining insertion and inverse sequence is utilized to avoid the algorithm from falling into local optima. Ultimately, non-dominated sorting ensures a set of well-distributed Pareto solutions. The experimental results of different problems indicate that the proposed algorithm can achieve better solutions than three classical algorithms (NSGAII, SPEA2 and HS) for the MOATALBP.