Evolving CBR and data segmentation by SOM for flow time prediction in semiconductor manufacturing factory |
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Authors: | Pei-Chann Chang Chin Yuan Fan Yen-Wen Wang |
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Affiliation: | (1) Department of Information Management, Yuan Ze University, No. 135,Yuan-Tung Rd., Chung-li, Tao-Yuan, 32026, Taiwan, R.O.C.;(2) Department of Industrial Engineering & Management, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-li, Tao-Yuan, 32026, Taiwan, R.O.C.;(3) Department of Industrial Engineering & Management, Ching-Yun University, Tao-Yuan, 32026, Taiwan, R.O.C. |
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Abstract: | Flow time of semiconductor manufacturing factory is highly related to the shop floor status; however, the processes are highly
complicated and involve more than 100 production steps. Therefore, a simulation model with the production process of a real
wafer fab located in Hsin-Chu Science-based Park of Taiwan is built for further studying of the relationship between the flow
time and the various input variables. In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Case-Based
Reasoning (CBR) for flow time prediction in semiconductor manufacturing factory is developed. And Genetic Algorithm (GA) is
applied to fine-tune the weights of features in the CBR model. The flow time and related shop floor status are collected and
fed into the SOM for clustering. Then, a corresponding SGA-CBR method is selected and applied for flow time prediction. Finally,
using the simulated data, the effectiveness of the proposed method (SGA-CBR) is shown by comparing with other approaches. |
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Keywords: | Due-date assignment Flow time prediction Case-based reasoning Genetic algorithms Self-organizing map |
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