Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 48, Issue 24, Pages 7371-7393Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207540903349013
Keywords
modular product family; configuration performance prediction; rough set; neural network ensemble; genetic algorithm
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Funding
- National Natural Science Foundation of China (NSFC) [50675082, 50705036]
- National Basic Research Program of China [2005CB724100]
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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.
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