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An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line

In recent years, the demand for printed circuit boards (PCBs) with various electronic components has tremendously increased due to high customization in smart electronic devices. To meet the market competition, efficient and realistic decisions with optimum planning and scheduling are required to en...

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Bibliographic Details
Published in:Expert systems with applications 2023-11, Vol.229, p.120600, Article 120600
Main Authors: Chen, Yarong, Zhong, Jingyan, Mumtaz, Jabir, Zhou, Shengwei, Zhu, Lixia
Format: Article
Language:English
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Summary:In recent years, the demand for printed circuit boards (PCBs) with various electronic components has tremendously increased due to high customization in smart electronic devices. To meet the market competition, efficient and realistic decisions with optimum planning and scheduling are required to enhance the productivity of PCB industries. Processing time of surface mounting technology (SMT) machines defines the pace of PCB production because of time consuming and critical process. In addition, it is observed that various planning and scheduling problems exist in PCB assembly line which are required to emphasize to increase the productivity. Therefore, in this study critical planning and scheduling problems i.e., component allocation problem (CAP) and component placement sequence problem (CPSP) are considered to optimize. Moreover, the periodic maintenance (PM) is also considered to maintain the efficiency of machines and make the problem more realistic. A mathematical model is formulated to minimize the maximum completion time, total energy consumption and the total maintenance time simultaneously. To solve this multi-objective NP-hard problem, an improved spider monkey optimization (ISMO) algorithm is developed. The efficiency and effectiveness of ISMO algorithm is improved by embedding few additional features. a) Two heuristics to ensure better initial solutions; b) evolutionary state judgment based on change in Pareto entropy to ensure the tradeoff between exploration and exploitation; c) an archive-based Q-Learning strategy to ensure the parametric adaptive tuning. Finally, computational experiments and comprehensive performance comparisons with other well-known multi-objective algorithms are performed with several size problem instances. The statistical results and analysis demonstrate that the proposed ISMO algorithm outperforms the other competitors.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120600