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Chien, C.-F., Chen, Y.-J., & Peng, J.-T. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496-509.
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A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence
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Manufacturing intelligence to forecast and reduce semiconductor cycle time
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An evolutionary approach to rehabilitation patient scheduling: A case study
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Chien, C.-F., Tseng, F., & Chen, C. (2008). An evolutionary approach to rehabilitation patient scheduling: A case study. European Journal of Operational Research, 189(3), 1234-1253.
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A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness
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Coordinated capacity migration and expansion planning for semiconductor manufacturing under demand uncertainties
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Hsu, C.-Y., Chien, C.-F., & Chen, P. (2012). Manufacturing intelligence for early warning of key equipment excursion for advanced equipment control in semiconductor manufacturing. Journal of the Chinese Institute of Industrial Engineer, 29(5), 303-313.
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Kuo, C.-J., Chien, C.-F., & Chen, C.-D. (2011). Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time. IEEE Transactions on Automation Science and Engineering, 8(1), 103-111.
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Cluster analysis of genome-wide expression data for feature extraction
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Liu, C.-W., & Chien, C.-F. (2013). An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing. Engineering Applications of Artificial Intelligence, 26(5-6), 1479-1486.
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Coordinating strategic outsourcing decisions for semiconductor assembly using a bi-objective genetic algorithm
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A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem
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Wu, J.-Z., Hao, X.-C., Chien, C.-F., & Gen, M. (2012b). A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem. Journal of Intelligent Manufacturing, 23(6), 2255-2270.
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