How to cite this paper
Chen, Y & Chiu, Y. (2014). Enhancing business intelligence for supply chain operations through effective classification of supplier management.Uncertain Supply Chain Management, 2(4), 229-236.
Refrences
Aliev, R. A., Fazlollahi, B., Guirimov, B. G., & Aliev, R. R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information Sciences, 177(20), 4241-4255.
Choi, B. I., & Chung-Hoon Rhee, F. (2009). Interval type-2 fuzzy membership function generation methods for pattern recognition. Information Sciences,179(13), 2102-2122.
Fan, M., Stallaert, J., & Whinston, A. B. (2003). Decentralized mechanism design for supply chain organizations using an auction market. Information Systems Research, 14(1), 1-22.
Hashemzadeh, G., Modiri, M., & Rahimi, Z. (2014). Identification and ranking effective factors on establishment of green supply chain management in railway industry. Uncertain Supply Chain Management. 2(4).
Hayes, J., & Finnegan, P. (2005). Assessing the of potential of e-business models: towards a framework for assisting decision-makers. European Journal of Operational Research, 160(2), 365-379.
Hwang, C., & Rhee, F. H. (2007). Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. Fuzzy Systems, IEEE Transactions on, 15(1), 107-120.
Karnik, N. N., & Mendel, J. M. (1999). Applications of type-2 fuzzy logic systems to forecasting of time-series. Information Sciences, 120(1), 89-111.
Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set.Information Sciences, 132(1), 195-220.
Radjou, N. (2002). Building an adaptive supply network. Supply-Chain World North America, April 22-24, New Orleans, LA.
Chung-Hoon Rhee, F. (2007). Uncertain fuzzy clustering: insights and recommendations. Computational Intelligence Magazine, IEEE, 2(1), 44-56.
Wu, D., & Mendel, J. M. (2009). Enhanced karnik--mendel algorithms. Fuzzy Systems, IEEE Transactions on, 17(4), 923-934.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.
Choi, B. I., & Chung-Hoon Rhee, F. (2009). Interval type-2 fuzzy membership function generation methods for pattern recognition. Information Sciences,179(13), 2102-2122.
Fan, M., Stallaert, J., & Whinston, A. B. (2003). Decentralized mechanism design for supply chain organizations using an auction market. Information Systems Research, 14(1), 1-22.
Hashemzadeh, G., Modiri, M., & Rahimi, Z. (2014). Identification and ranking effective factors on establishment of green supply chain management in railway industry. Uncertain Supply Chain Management. 2(4).
Hayes, J., & Finnegan, P. (2005). Assessing the of potential of e-business models: towards a framework for assisting decision-makers. European Journal of Operational Research, 160(2), 365-379.
Hwang, C., & Rhee, F. H. (2007). Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. Fuzzy Systems, IEEE Transactions on, 15(1), 107-120.
Karnik, N. N., & Mendel, J. M. (1999). Applications of type-2 fuzzy logic systems to forecasting of time-series. Information Sciences, 120(1), 89-111.
Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set.Information Sciences, 132(1), 195-220.
Radjou, N. (2002). Building an adaptive supply network. Supply-Chain World North America, April 22-24, New Orleans, LA.
Chung-Hoon Rhee, F. (2007). Uncertain fuzzy clustering: insights and recommendations. Computational Intelligence Magazine, IEEE, 2(1), 44-56.
Wu, D., & Mendel, J. M. (2009). Enhanced karnik--mendel algorithms. Fuzzy Systems, IEEE Transactions on, 17(4), 923-934.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.