A Cuckoo Search Clustering Algorithm for Design Structure Matrix

Hayam G. Wahdan, Sally S. Kassem, Hisham M. Abdelsalam

2016

Abstract

Modularity is a concept that is applied to manage complex systems by breaking them down into a set of modules that are interdependent within and independent across the modules. Benefits of modularity are often achieved from module independence that allows for independent development to reduce overall lead time and to reach economies of scale due to sharing similar modules across products in a product family. The main objective of this paper is to support design products under modularity, cluster products into a set of modules or clusters, with maximum internal relationships within a given module and minimum external relationships with other modules. The product to be designed is represented in the form of a Design Structure Matrix (DSM) that contains a list of all product components and the corresponding information exchange and dependency patterns among these components. In this research Cuckoo Search (CS) optimization algorithm is used to find the optimal number of clusters and the optimal assignment of each component to specific cluster in order to minimize the total coordination cost. Results obtained showed an improved performance compared to published studies.

References

  1. Abdelsalam, H. M., Rasmy, M. H., & Mohamed, H. G. (2014). A Simulation-Based Time Reduction Approach for Resource Constrained Design Structure Matrix. International Journal of Modeling and Optimization , 4 (1), 51-55.
  2. Abdelsalam, H., & Bao, H. (2006). A Simulation-based Optimization Framework for Product Development Cycle Time Reduction. IEEE Transactions on Engineering Management , 53 (1), 69-85.
  3. Aguwa, C. C., Monplaisir, L., & Sylajakumar, P. A. (2012). Effect of Rating Modification on a FuzzyBased Modular Architecture for Medical Device Design and Development. Advances in Fuzzy Systems .
  4. Borjesson, F., & Hölttä-Otto, K. (2012). Improved clustering algolrithm for design structure matrix. ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (pp. 1-10). Chicago, IL, USA: IDETC/CIE 2012.
  5. Borjesson, F., & ltta-Otto, K. H. (2014). A module generation algorithm for product architecture based on component interactions and strategic drivers. Research in Engineering Design , 25 (1), 31-51.
  6. Borjesson, F., & Sellgren, U. (2013). Fast Hybrid Genetic Clustering Algorithm for Design Structure Matrix. 25th International Conference on Design Theory and Methodology. Portland, Oregon, USA: ASME 2013.
  7. Borjesson, F. (2009). Improved output in modular function deployment using heuristics. International conferance on engineering design, (pp. 24-27). Stanford,USA.
  8. Burnwal, S., & Deb, S. (2012). Scheduling optimization of flexible manufacturing system using cuckoo searchbased approach. The International Journal of Advanced Manufacturing Technology, 64, 1-9.
  9. Chen, H., Li, S., & Tang, Z. (2011). Hybrid gravitational search algorithm with random-key encoding scheme combined with simulated annealing. International Journal of Computer Science and Mobile Computing, 11 (6), 208-217.
  10. Eppinger, S., Whitney, D., Smith, R., & Gebala, D. (1994). A model based method for organizing tasks in product development. Research in Engineering Design , 1-13.
  11. Gutierrez, C. I. (1998). Integration analysis of product architecture to support effective team co-location. Cambridge: Masters thesis, Massachusetts Institute of Technology.
  12. Gwangwava, N., Nyadongo, S., Mathe, C., & Mpof, K. (2013). Modular Clusterization Product Design Support System. International Journal of Advances in Computer Science and Technology (IJACST) , 2 (11), 8-13.
  13. Idicula, J. (1995). Planning for concurrent engineering. Singapore: Gintic Institute Research .
  14. Jung, S., & Simpson, T. W. (2014). A Clustering Method Using New Modularity Indices and Genetic Algorithm with Extended Chromosomes. DSM 14 Proceedings of the 16th International DSM conference: Risk and Change management in complex systems, (pp. 167- 176).
  15. Kim, S., Baek, J. W., Moon, S. K., & Jeon, S. M. (2015). A New Approach for Product Design by Integrating Assembly and Disassembly Sequence Structure Planning. 247-257.
  16. Li, X., & Yin, M. (2015). Modified cuckoo search algorithm with self adaptive parameter method. Information Sciences , 298, 80-97.
  17. Navimipour, N. J., & Milani, F. S. (2015). Task Scheduling in the Cloud Computing Based on the Cuckoo Search Algorithm. International Journal of Modeling and Optimization , 5 (1), 44-47.
  18. Pandremenos, J., & Chryssolouris, G. (2012). A neural network approach for the development of modular product architectures. International Journal of Computer Integrated Manufacturing, 1-8.
  19. Thebeau, R. (2001). Knowledge management of system interfaces and interactions for product development process. Massachusetts Institute of Technology.
  20. van Beek, T. J., Erden, M. S., & Tomiyama, T. (2010). Modular design of mechatronic systems with function modeling. Mechatronics , 20 (8), 850-863.
  21. Verma, R., & Kumar, S. (2012). DNA sequence assembly using continuous particle swarm optimization with smallest position value rule. First International Conference on Recent Advances in Information Technology, (pp. 410-415 ).
  22. Yang, Q., Yao, T., Lu, T., & Zhang, B. (2014). An Overlapping-Based Design Structure Matrix for Measuring Interaction Strength and Clustering Analysis in Product Development Project. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT , 61 (1), 159-170.
  23. Yang, X., & Deb, S. (2009). Cuckoo search via Levy flights. the World Congress on Nature and Biologically Inspired Computing (NABIC 7809) (pp. 210-214). Coimbatore, India: IEEE.
  24. Yang, X.-s., & Deb, S. (2010). Engineering Optimisation by Cuckoo Search. International Journal of Math Model Numerical Optimization, 1 (4), 330-343.
  25. Yassine, A. A., Yu, T.-L., , & Goldberg, D. E. (2007). An information theoretic method for developing modular architectures using genetic algorithms. Research in Engineering Design , 18, 91-109.
  26. Yildiz, A. R. (2013). Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. international Journal of Advanced Manufacturing Technology , 64 (1), 55-61.
Download


Paper Citation


in Harvard Style

G. Wahdan H., S. Kassem S. and M. Abdelsalam H. (2016). A Cuckoo Search Clustering Algorithm for Design Structure Matrix . In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-171-7, pages 36-43. DOI: 10.5220/0005693000360043


in Bibtex Style

@conference{icores16,
author={Hayam G. Wahdan and Sally S. Kassem and Hisham M. Abdelsalam},
title={A Cuckoo Search Clustering Algorithm for Design Structure Matrix},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2016},
pages={36-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005693000360043},
isbn={978-989-758-171-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A Cuckoo Search Clustering Algorithm for Design Structure Matrix
SN - 978-989-758-171-7
AU - G. Wahdan H.
AU - S. Kassem S.
AU - M. Abdelsalam H.
PY - 2016
SP - 36
EP - 43
DO - 10.5220/0005693000360043