Author
Listed:
- Honggen Chen
(Zhengzhou University of Aeronautics
Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)
- Pengxiang Wang
(Zhengzhou University of Aeronautics
Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)
- Jing Li
(Zhengzhou University of Aeronautics
Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)
- Guohui Zhang
(Zhengzhou University of Aeronautics
Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)
- Yan Zhang
(Zhengzhou University of Aeronautics
Zhengzhou University of Aeronautics)
Abstract
To address the challenges of significant logistics crossings and low production efficiency in traditional cluster layouts, a cellular manufacturing system (CMS) is commonly employed in diverse, small-batch production processes due to its high flexibility and adaptability. This study presents a comprehensive approach to effectively transform cluster layouts into cell manufacturing layouts, addressing the associated challenges. Initially, an improved fuzzy C-means clustering algorithm, enhanced with the elbow and the dissimilarity coefficient methods, is applied for cell division. Subsequently, a bi-objective optimization model is developed to minimize both the logistics distance and the layout area, with the NSGA-II-SA algorithm specifically tailored to handle the bi-objective sampling criterion. Thereafter, the layout optimization is performed, focusing on both the order and direction of the intracellular facilities. By applying the elbow method to the part-equipment matrix across various dimensions, its effectiveness in determining the optimal number of cell partitions is validated. Finally, the whole process of transforming the cluster layout into a CMS is successfully executed. The results demonstrate that the proposed algorithm outperforms non-dominated sorting genetic algorithm II (NSGA-II), the simulated annealing (SA) algorithm using random sampling (RM_SA), and the SA algorithm using bi-objective sampling (TM_SA) algorithms in both searchability and overall performance.
Suggested Citation
Honggen Chen & Pengxiang Wang & Jing Li & Guohui Zhang & Yan Zhang, 2025.
"An improved NSGAII-SA algorithm for the cell manufacturing system layout optimization problem,"
Operational Research, Springer, vol. 25(1), pages 1-31, March.
Handle:
RePEc:spr:operea:v:25:y:2025:i:1:d:10.1007_s12351-025-00899-0
DOI: 10.1007/s12351-025-00899-0
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:25:y:2025:i:1:d:10.1007_s12351-025-00899-0. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.