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.