Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Adaptive attribute selection for configurator design via shapley value
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - Configuration
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Product configurators are widely used to elicit custmer requirements in the form of tangible product specification in product design. However customers are heterogeneous in terms of preferences and needs. To meet the diversified customer needs, companies have provided a wider variety of product variants. In this situation, there may be too many choices for customers to specify during configuration process. The huge number of choices may lead to mass confusion among customers, and less customer satisfaction. To handle this issue, we propose a hierarchical product configuration design method which operates in a from-coarse-to-fine manner. K-means method is used to classify the whole product attribute sets into several interested clusters. The coarse configuration process will be used to identify customers' preferences cluster. The fine configuration stage will be conducted within each cluster to fine tune the product configuration. Thus the confusion caused by the large number of choices can be mitigated.