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

DOI10.1080/00207540903349013zbMath1210.90084OpenAlexW1963796078MaRDI QIDQ3168936

Xinyu Shao, Fanmao Liu, Guo-Jun Zhang, Hai-Ping Zhu

Publication date: 27 April 2011

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207540903349013

zbMATH Keywords

genetic algorithmrough setneural network ensembleconfiguration performance predictionmodular product family

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Production models (90B30)

Related Items (1)

Optimal supply chain design with product family: a cloud-based framework with real-time data consideration

Cites Work

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• Minimal approximate hitting sets and rule templates
• Data-mining-based methodology for the design of product families
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• Bootstrap Techniques for Error Estimation
• Integrating data mining and rough set for customer group-based discovery of product configuration rules
• DAKA: design activity knowledge acquisition through data-mining

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