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

PLoS One

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

Published: December 2024

AI Article Synopsis

  • The paper focuses on optimizing layouts for multi-layer circular manufacturing cells (MCMC), which have been overlooked compared to single-layer cells (SCMC).
  • Existing approaches primarily use NSGA II, but due to MCMC's complexity with more objectives, this study proposes employing the NSGA III algorithm for better multi-objective optimization.
  • Validation using data from a smart factory in Zhejiang, China, shows that NSGA III offers significantly better layout solutions, contributing to advancements in smart factory design and enhancing MCMC research methodologies.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666037PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312364PLOS

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

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