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基于NSGA III的多层环形制造单元设备布局研究

Research on equipment layout of multi-layer circular manufacturing cell based on NSGA III.

作者信息

Zhao Yanlin

机构信息

School of Intelligent Manufacturing, Panzhihua University, Panzhihua, Sichuan, P. R. China.

出版信息

PLoS One. 2024 Dec 23;19(12):e0312364. doi: 10.1371/journal.pone.0312364. eCollection 2024.

Abstract

This paper investigates the layout optimization of multi-layer circular manufacturing cells (MCMC), a topic that has garnered limited attention compared to single-layer circular manufacturing cells (SCMC). With the continuous advancement of global intelligent manufacturing, MCMC has emerged as a viable solution, with several smart factories already implementing this model. Existing literature predominantly utilizes the NSGA II algorithm for SCMC layouts due to their relatively few objectives. However, the layout problem for MCMC encompasses a significantly larger number of objectives, rendering NSGA II inadequate. This study aims to fill this research gap by proposing an innovative approach using NSGA III, specifically designed for complex multi-objective optimization. A multi-dimensional target mathematical model for MCMC is established, facilitating the systematic examination of layout configurations. The methodology employs NSGA III to effectively tackle the challenges posed by MCMC layouts. To validate the effectiveness of NSGA III, empirical data from a smart factory in Zhejiang, China, is utilized. The findings demonstrate that NSGA III significantly outperforms traditional algorithms, yielding superior solutions for MCMC layout problems. This research not only challenges the conventional SCMC layout paradigm but also expands the options available for facility layouts in smart factories. Ultimately, it addresses the pressing engineering needs of smart factory construction and contributes valuable insights to the field of MCMC research, establishing a robust methodology for future studies.

摘要

本文研究多层圆形制造单元(MCMC)的布局优化,与单层圆形制造单元(SCMC)相比,该主题受到的关注有限。随着全球智能制造的不断发展,MCMC已成为一种可行的解决方案,已有几家智能工厂采用了这种模式。由于SCMC布局的目标相对较少,现有文献主要使用NSGA II算法进行布局。然而,MCMC的布局问题包含大量目标,使得NSGA II并不适用。本研究旨在通过提出一种使用NSGA III的创新方法来填补这一研究空白,该方法专门为复杂的多目标优化而设计。建立了MCMC的多维目标数学模型,便于系统地研究布局配置。该方法采用NSGA III有效应对MCMC布局带来的挑战。为验证NSGA III的有效性,利用了中国浙江一家智能工厂的实证数据。研究结果表明,NSGA III明显优于传统算法,能为MCMC布局问题提供更优解决方案。本研究不仅挑战了传统的SCMC布局范式,还扩展了智能工厂设施布局的选择。最终,它满足了智能工厂建设迫切的工程需求,为MCMC研究领域提供了有价值的见解,为未来研究建立了一种可靠的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59aa/11666037/5525730bfc4f/pone.0312364.g001.jpg

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