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Solutions for product configuration management: An empirical study

  • JINN-YI YEH (a1) and TAI-HSI WU (a1)

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.

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Corresponding author

Reprint requests to: Jinn-Yi Yeh, Department of Industrial Engineering and Technology Management, Da-Yeh University, 112 Shan-Jiau Road, Da-Tsuen, Changhua, Taiwan 515, ROC. E-mail: jyeh@mail.dyu.edu.tw

References

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Keywords

Solutions for product configuration management: An empirical study

  • JINN-YI YEH (a1) and TAI-HSI WU (a1)

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