Hostname: page-component-77f85d65b8-6bnxx Total loading time: 0 Render date: 2026-04-22T08:40:48.557Z Has data issue: false hasContentIssue false
  • English
  • Français

A shortest path method for sequential change propagations in complex engineering design processes

Published online by Cambridge University Press:  09 June 2015

Yuliang Li ,
Wei Zhao  and
Yongsheng Ma
Yuliang Li*
Affiliation:
State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
Wei Zhao
Affiliation:
Department of Foreign Language, Zhejiang University of Finance and Economics, Hangzhou, China
Yongsheng Ma
Affiliation:
Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada
*
Reprint requests to: Yuliang Li, State Key Lab of CAD&CG, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province 310027, China. E-mail: lyl_zw@zju.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Engineering design changes constantly occur in complex engineering design processes. Designers need appropriate measures to handle the numerous design changes in order to realize consistent and completely validated product models so that successful product development is assured. In this paper, a time-based mathematic model is presented to characterize the sequential change propagation process, and then the shortest path algorithm is given to find the most timesaving routes for changes to propagate to other dependent design tasks. An analysis method is introduced to compute the sensitivities of change impacts on the affected design tasks, which indicates that the more time consumed by a change to take its effect, the more sensitive the change impacts on those downstream dependent tasks. A case study of change propagations in motorcycle engine design process was presented to demonstrate the proposed method.

Keywords

Change PropagationDesign ProcessSensitivity AnalysisShortest Path Method

Information

Type
Regular Articles
Information
AI EDAM , Volume 30 , Issue 1 , February 2016 , pp. 107 - 121
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Bhise, V. (2014). Designing Complex Products with Systems Engineering Processes and Techniques. London: CRC Press.Google Scholar
Bowman, R. (1995). Efficient estimation of arc criticalities in stochastic activity networks. Management Science 41(1), 58–67.CrossRefGoogle Scholar
Browning, T., & Eppinger, S. (2002). Modeling impacts of process architecture on cost and schedule risk in product development. IEEE Transactions on Engineering Management 49(4), 428–442.CrossRefGoogle Scholar
Clarkson, J., Simons, C., & Eckert, C. (2004). Predicting change propagation in complex design. Transactions of the ASME Journal of Mechanical Design 126(5), 788–797.CrossRefGoogle Scholar
Dijkstra, V. (1959). A note on two problems in connection with graphs. Numerische Mathematik 1, 269–271.CrossRefGoogle Scholar
Dodin, B., & Elmaghraby, A. (1985). Approximating the criticality of the activities in pert networks. Management Science 31(2), 207–223.CrossRefGoogle Scholar
Eckert, C., Clarkson, J., & Zanker, W. (2004). Change and customization in complex engineering domains. Research in Engineering Design 15(1), 1–21.CrossRefGoogle Scholar
Eppinger, S., Whitney, D., Smith, R., & Gebala, D. (1994). A model-based method for organizing tasks in product development. Research in Engineering Design 6(1), 1–13.CrossRefGoogle Scholar
Giffin, M., de weck, O., Bounova, G., Keller, R., Eckert, C., & Clarkson, J. (2009). Change propagation analysis in complex technical systems. Transactions of ASME Journal of Mechanical Design 131(8), 081001.CrossRefGoogle Scholar
Hamraz, B., Caldwell, N., & Clarkson, J. (2013 a). A matrix-calculation-based algorithm for numerical change propagation analysis. IEEE Transactions on Engineering Management 60(1), 186–198.CrossRefGoogle Scholar
Hamraz, B., Caldwell, N., & Clarkson, J. (2013 b). A holistic categorization framework for literature on engineering change management. Systems Engineering 16(4), 473–505.CrossRefGoogle Scholar
Jarratt, T., Eckert, C., Caldwell, N., & Clarkson, P. (2011). Engineering change: an overview and perspective on the literature. Research in Engineering Design 22(2), 103–124.CrossRefGoogle Scholar
Kerzner, H. (2009). Project Management: A Systems Approach to Planning, Scheduling and Controlling. Hoboken, NJ: Wiley.Google Scholar
Krishnan, V. (1996). Managing the simultaneous execution of coupled phases in concurrent product development. IEEE Transactions on Engineering Management 43(2), 210–217.CrossRefGoogle Scholar
Lee, H., Seol, H., Sung, N., Hong, Y., & Park, Y. (2010). An analytic network process approach to measuring design change impacts in modular products. Journal of Engineering Design 21(1), 75–91.CrossRefGoogle Scholar
Li, Y., Zhao, W., & Shao, X. (2012). A process simulation based method for scheduling product design change propagation. Advanced Engineering Informatics 26(3), 529–538.CrossRefGoogle Scholar
Mummolo, G. (1997). Measuring uncertainty and criticality in network planning by PERT-path technique. International Journal of Project Management 15(6), 377–387.CrossRefGoogle Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. H. (2007). Engineering Design: A Systematic Approach, 3rd ed.London: Springer.CrossRefGoogle Scholar
Siddiqi, A., Nounova, G., de weck, O., Keller, R., & Robinson, B. (2011). A posteriori design change analysis for complex engineering projects. Transactions of ASME Journal of Mechanical Design 133(10), 101005.CrossRefGoogle Scholar
Tang, D. (2009). DSM Based Product Design and Development. Beijing: Science Press (in Chinese).Google Scholar
Viteri, J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269.CrossRefGoogle Scholar
Wilson, J. (1996). Introduction to Graph Theory, 4th ed.Essex: Addison–Wesley.Google Scholar
Wynn, D.C., Caldwell, N., & Clarkson, J. (2010). Can change prediction help prioritise redesign work in future engineering systems? Proc. Int. Design Conf.—DESIGN 2010, Croatia, May 17–20.Google Scholar
Yang, F., & Duan, G. (2012). Developing a parameter linkage-based method for searching change propagation paths. Research in Engineering Design 23(4), 353–372.CrossRefGoogle Scholar