Hostname: page-component-65f69f4695-xtrld Total loading time: 0 Render date: 2025-06-28T04:24:41.948Z Has data issue: false hasContentIssue false
  • English
  • Français

Solutions for product configuration management: An empirical study

Published online by Cambridge University Press:  22 July 2005

JINN-YI YEH  and
TAI-HSI WU
JINN-YI YEH
Affiliation:
Department of Industrial Engineering and Technology Management, Da-Yeh University, 112 Shan-Jiau Road, Da-Tsuen, Changhua, Taiwan 515, Republic of China
TAI-HSI WU
Affiliation:
Department of Industrial Engineering and Technology Management, Da-Yeh University, 112 Shan-Jiau Road, Da-Tsuen, Changhua, Taiwan 515, Republic of China
Get access Rights & Permissions [Opens in a new window]

Abstract

Customers can directly express their preferences on many options when ordering products today. Mass customization manufacturing thus has emerged as a new trend for its aiming to satisfy the needs of individual customers. This process of offering a wide product variety often induces an exponential growth in the volume of information and redundancy for data storage. Thus, a technique for managing product configuration is necessary, on the one hand, to provide customers faster configured and lower priced products, and on the other hand, to translate customers' needs into the product information needed for tendering and manufacturing. This paper presents a decision-making scheme through constructing a product family model (PFM) first, in which the relationship between product, modules, and components are defined. The PFM is then transformed into a product configuration network. A product configuration problem assuming that customers would like to have a minimum-cost and customized product can be easily solved by finding the shortest path in the corresponding product configuration network. Genetic algorithms (GAs), mathematical programming, and tree-searching methods such as uniform-cost search and iterative deepening A* are applied to obtain solutions to this problem. An empirical case is studied in this work as an example. Computational results show that the solution quality of GAs retains 93.89% for a complicated configuration problem. However, the running time of GAs outperforms the running time of other methods with a minimum speed factor of 25. This feature is very useful for a real-time system.

Keywords

ConfigurationGenetic AlgorithmMathematical ProgrammingProduct Family ModelTree Searching

Information

Type
PRACTICUM PAPER
Information
AI EDAM , Volume 19 , Issue 1 , February 2005 , pp. 39 - 47
Copyright
2005 Cambridge University Press

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

Anderson, D.M. (1997). Agile Product Development for Mass Customization. Chicago: Irwin Professional Publications.
Barker, V.E., O'Connor, D.E., & Bachant, J. (1989). Expert systems for configuration at digital: XCON and beyond. Communications of the ACM 32(3), 298–318.CrossRefGoogle Scholar
Bazaraa, M.S., Jarvis, J.J., & Sherali, H.D. (1990). Linear Programming and Network Flows. New York: Wiley.
Brown, D.C. (1998). Defining configuring. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, 301–305.CrossRefGoogle Scholar
Chao, P.Y. & Chen, T.T. (2001). Analysis of assembly through product configuration. Computers in Industry 44, 189–203.CrossRefGoogle Scholar
Chen, L. & Lin, L. (2002). Optimization of product configuration design using functional requirements and constraints. Research in Engineering Design 13, 167–182.CrossRefGoogle Scholar
Choi, I. & Bae, S. (2001). An architecture for active product configuration management in industrial virtual enterprises. The International Journal of Advanced Manufacturing Technology 18, 133–139.CrossRefGoogle Scholar
Fleischanderl, G., Friedrich, G.E., Haselbock, A., Schreiner, H., & Stumptner, M. (1998). Configuring large systems using generative constraint satisfaction. IEEE Intelligent Systems 13(4), 59–68.CrossRefGoogle Scholar
Fohn, S.M., Liau, J.S., Greef, A.R., Young, R.E., & O'Grady, P.J. (1995). Configuration computer systems through constraint-based modeling and interactive constraint satisfaction. Computers in Industry 27, 3–21.CrossRefGoogle Scholar
Forza, C. & Salvador, F. (2002). Managing for variety in the order acquisition and fulfillment process: The contribution of product configuration systems. International Journal of Production Economics 76, 87–98.CrossRefGoogle Scholar
Frayman, F. & Mittal, S. (1987). Cossack: A constraint-based expert system for configuration tasks. In Knowledge-Based Expert Systems in Engineering: Planning and Design (Sriram, D. & Adey, R.A., Eds.), pp. 143–146. Southampton: Computational Mechanics Publications.
Globerson, S. (1997). Discrepancies between customer expectations and product configuration. International Journal of Project Management 15(4), 199–203.CrossRefGoogle Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison–Wesley.
Haupt, R.L. & Haupt, S.E. (1998). Practical Genetic Algorithms. New York: Wiley–Interscience.
Hu, J. & Goodman, E. (2004). Wireless access point configuration by genetic programming. Proc. 2004 IEEE Congress on Evolutionary Computation, pp. 1178–1184. Portland, OR: IEEE Press.
Hulubei, T., Freuder, E.C., & Wallace, R.J. (2003). The Goldilocks problem. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, 3–11.CrossRefGoogle Scholar
Jiao, J. & Tseng, M.M. (2000). Fundamentals of product family architecture. Integrated Manufacturing Systems 11(7), 469–483.CrossRefGoogle Scholar
Junker, U. & Mailharro, D. (2003). Preference programming: Advanced problem solving for configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, 13–29.CrossRefGoogle Scholar
Kusiak, A. & Huang, C.C. (1996). Development of modular products. IEEE Transactions on Components, Packaging, and Manufacturing Technology, Part A 19, 523–538.CrossRefGoogle Scholar
Lowe, H., Pechoucek, M., & Bundy, A. (1998). Proof planning for maintainable configuration systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, 345–356.CrossRefGoogle Scholar
Mailharro, D. (1998). A classification and constraint-based framework for configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, 387–397.Google Scholar
Mitchell, M. (1999). An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press.
Russell, S. & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice–Hall.
Stonebraker, P.W. (1996). Restructuring the bill of material for productivity: A strategic evaluation of product configuration. International Journal of Production Economics 45, 251–260.CrossRefGoogle Scholar
Svensson, C. & Barfod, A. (2002). Limits and opportunities in mass customization for “build to order” SMEs. Computers in Industry 49, 77–89.CrossRefGoogle Scholar
Turban, E. (1992). Expert Systems and Applied Artificial Intelligence. New York: Macmillan.