Cluster based evolving FCBR for flow time prediction in semiconductor manufacturing factory

Authors:
Chen-Hao Liu;Pei-Chann Chang;I-Wei Kao
Affiliations:
Department of Information Management, Kai-Nan University, Taoyuan, Taiwan, R.O.C.;Department of Information Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.
Venue:
ACS'08 Proceedings of the 8th conference on Applied computer scince
Year:
2008

Citing 4
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Abstract

In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Evolving Fuzzy Case-Based Reasoning (EFCBR) for flow time prediction is proposed. Genetic Algorithms (GAs) is applied to fine-tune the fuzzy term numbers and weights of fuzzy features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, corresponding EFCBR is applied for flow time prediction. The effectiveness of the proposed method (SEFCBR) is shown by comparing with other approaches.