Product modular analysis with design structure matrix using a hybrid approach based on MDS and clustering
| العنوان: | Product modular analysis with design structure matrix using a hybrid approach based on MDS and clustering |
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| المؤلفون: | Mahmoud Efatmaneshnik, Shraga Shoval, Li Qiao, Michael J. Ryan |
| المساهمون: | Qiao, Li, Efatmaneshnik, Mahmoud, Ryan, Michael, Shoval, Shraga |
| المصدر: | Journal of Engineering Design. 28:433-456 |
| بيانات النشر: | Informa UK Limited, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | multidimensional scaling, 0209 industrial biotechnology, Engineering, 0211 other engineering and technologies, Engineering, Multidisciplinary, 02 engineering and technology, computer.software_genre, Design structure matrix, 020901 industrial engineering & automation, Product lifecycle, Component (UML), Multidimensional scaling, Cluster analysis, modular analysis, 021106 design practice & management, Product design, business.industry, General Engineering, Modular design, ComputingMethodologies_PATTERNRECOGNITION, Product (mathematics), Data mining, Artificial intelligence, product architecture, business, computer, design structure matrix, clustering |
| الوصف: | Modular analysis using the Design Structure Matrix (DSM) identifies the interactions between groups of components, and clusters them into modules in order to achieve competitive advantages in the product design processes. In this paper, a hybrid approach, based on multidimensional scaling (MDS) and clustering methods, is applied to component DSM for product architecting. The motivation is to facilitate better modularizations that enhance different product attributes in various product lifecycle stages. An experimental framework is developed to evaluate the performance of MDS clustering. Three MDS methods and four ubiquitous clustering methods are compared to reveal the most suitable for DSMs. The experimental results with several examples demonstrate that the effectiveness of MDS clustering, and show the superiority of non-metric MDS, SMACOF (Scaling by MAjorizing a Complicated Function) and hierarchical/cosine methods. These methods are capable of partitioning the product architecture into a set of modules and outperform the Newman-Girven algorithm, which has been extensively applied to DSM clustering. The proposed method is capable of partitioning the product architecture into reasonable modules. In addition, it can produce the optimal modules for any number of clusters, which is favourable especially when the cluster number is a higher managerial decision. Refereed/Peer-reviewed |
| تدمد: | 1466-1837 0954-4828 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a24d02e2fd21bbf69d082b13c91501c0Test https://doi.org/10.1080/09544828.2017.1325858Test |
| رقم الانضمام: | edsair.doi.dedup.....a24d02e2fd21bbf69d082b13c91501c0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14661837 09544828 |
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