Integration of rough set and neural network ensemble to predict the configuration performance of a modular product family

Abstract

Configuration performance prediction (CPP) is critical in the whole process of configuration design for a modular product family. Its aim is to estimate the key performance parameter values in advance, thus evaluating if the product variant can satisfy the customers’ personalised requirements or not. In this paper, we propose a novel prediction approach based on the integration of rough set and neural network ensemble through discovering the knowledge from the historical configuration information table. The minimal hitting set is introduced and its equivalence relationship with the minimal attribute reduction is proven. A genetic algorithm is designed to perform the approximate reduction of the condition attributes. A neural network ensemble model used for regression prediction is constituted by means of the variant bagging method based on error clustering. This methodology can reuse the discovered configuration rules and knowledge efficiently, as well as reduce the effort of experimental measurement to some extent. Finally, the applicability of this prediction method is verified on a newly developed refrigerator family.

Keywords:

• modular product family
• configuration performance prediction
• rough set
• neural network ensemble
• genetic algorithm

Acknowledgements

The authors greatly acknowledge the financial support from the National Natural Science Foundation of China (NSFC) with the grant numbers 50675082 and 50705036, and the National Basic Research Program of China with the grant number 2005CB724100. The authors also thank the editor and the referees for their valuable comments and suggestions that have led to a substantial improvement of the paper.