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Multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors

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Abstract

Intelligent manufacturing can provide powerful support for the digital transformation of manufacturing industry. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive industry because of their small size, low cost, and high reliability. Aiming at the problems of low flexibility, poor adaptability, and high manufacturing cost in the mixed-flow intelligent production line of multi-variety automotive MEMS pressure sensors in this study, a multi-objective optimization model is established with takt time and balance rate as optimization objectives. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to obtain the multi-objective optimization of the mixed-flow intelligent production line with the elite strategy, crowding degree, and crowded comparison operator. The accuracy of the NSGA-II is validated by comparing it with that of the ant colony optimization (ACO) algorithm, simulated annealing (SA) algorithm, and particle swarm optimization (PSO) algorithm. The NSGA-II achieves higher optimization accuracies for takt time and balance rate compared to ACO, SA, and PSO algorithms. Specifically, the NSGA-II achieves optimization accuracies of 2.73%, 2.44%, and 8.99% for the takt time, slightly surpassing those of the ACO, SA, and PSO algorithms respectively. Similarly, for the balance rate, the NSGA-II achieves optimization accuracies of 2.17%, 1.89%, and 2.48%, slightly higher than those of the ACO, SA, and PSO algorithms respectively. The takt time is optimized by NSGA-II to less than 10 s/piece, while the balance rate is optimized to over 90%. The multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors is practical and instructive for improving production line efficiency.

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Data availability

The data set used in this study is not publicly available at this time and may be obtained from the corresponding author upon reasonable request and permission of the authors.

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Acknowledgements

The work was supported by Wuhan East Lake District Unveiling the List of Hanging Project (No. 2022KJB101), and Hubei Key Laboratory of Electronic Manufacturing and Packaging Integration (No. EMPI2023024).

Funding

Wuhan East Lake District Unveiling the List of Hanging Project, 2022KJB101, Hui Li, Hubei Key Laboratory of Electronic Manufacturing and Packaging Integration, EMPI2023024, Shengnan Shen.

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Quanyong Zhang contributed by proposing ideas and methods, conducting experimental verification, and writing the manuscript. Hui Li and Shengnan Shen supervise methods, manuscript checking, and project administration. Wan Cao, Jing Jiang, Wen Tang, and Yuanshun Hu contributed to data collection.

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Correspondence to Hui Li or Shengnan Shen.

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Zhang, Q., Li, H., Shen, S. et al. Multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors. Appl Intell 55, 72 (2025). https://doi.org/10.1007/s10489-024-05928-7

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