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A Concurrent Optimization Model for Supplier Selection with Fuzzy Quality Loss

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DOI: 
https://doi.org/10.3926/jiem.800
Abstract (2. Language): 
Purpose: The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.
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REFERENCES

References: 

Barthelemy, J. (2003). The Seven Deadly Sins of Outsourcing. Academy Of Management Executive, 17(2),
87-98. https://doi.org/10.5465/AME.2003.10025203
Bayrak, M.Y., Celebi, N., & Taskin, H. (2007). A Fuzzy Approach for Supplier Selection. Production
Planning and Control, 18(1), 54-63. https://doi.org/10.1080/09537280600940713
Cao, Y., Mao, J., Yang, J., Wu, Z., & Wu, L. (2006). A Robust Tolerance Design Method Based on Fuzzy
Quality Loss. Frontier of Machanical Engineering China, 1, 101-105. https://doi.org/10.1007/s11465-005-0010-y
Cao, Y., Mao, J., Ching, H., & Yang, J. (2009). A Robust Tolerance Optimization Method Based on Fuzzy
Quality Loss. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering
Science, 223, 2647-2653. https://doi.org/10.1243/09544062jmes1451
Chen, F., Tzeng, Y., Hsu, M., & Chen, W. (2010). Combining Taguchi Method, Principal Component
Analysis and Fuzzy Logic to The Tolerance Design of a Dual-Purpose Six Bar Mechanism. Transactions
of the Canadian Society for Mechanical Engineering, 34(2).
Feng, C.X., Wang, J., & Wang, J.S. (2001). An Optimization Model for Concurrent Selection of Tolerances
and Supplier. Computers and Industrial Engineering, 40, 15-33. https://doi.org/10.1016/S0360-8352(00)00047-4
Ghorbeni, M., Bahrami, M., & Arabzad, S.M. (2012). Integrated Model for Supplier Selection and Order
Allocation; Using Shannon Enthropy, SWOT, and Linear Programming. Procedia Social and Behavioral
Sciences, 14, 521-527. https://doi.org/10.1016/j.sbspro.2012.04.064 Guneri, A.F., & Kuzu, A. (2009). Supplier Selection by Using A Fuzzy Approach in Just-In-Time: A Case
Study. International Journal of Computer Integrated Manufacturing, 22(8), 774-783.
https://doi.org/10.1080/09511920902741075
Hsieh, K.L. (2007). Applying Fuzzy Set Approach into Achieving Quality Improvement for Qualitative
Quality Response. Proceedings of the 2007 WSEAS International Conference on Computer Engineering and
Applications, Gold Coast, Australia.
Kahraman, C., Ertay, T., & Büyüközkan, G. (2004). A Fuzzy Optimization Model for QFD Planning
Process Using Analytic Network Approach. European Journal of Operational Research.
Linn, R.J., Tsung, F., & Ellis, L.W.C. (2006). Supplier Selection Based on Process Capability and Price
Analysis. Quality Engineering, 18, 123-129. https://doi.org/10.1080/08982110600567475
Monfared, M.A.S., & Dadashian, F. (2005). Design of A New Quality Assessment System Using Fuzzy
Taguchi Functions. Proceedings of the 17th IMACS Worldd Congress, Paris.
Nukala, S., & Gupta, S.M. (2007). A Fuzzy Mathematical Programming Approach for Supplier Selection
in a Closed-Loop Supply Chain Network. Proceedings of the 2007 POM Dallas Meeting.
Pi, W., & Low, C. (2006). Supplier Evaluation and Selection via Taguchi Loss Functions and an AHP.
International Journal of Advanced Manufacturing Technology, 27, 625-630. https://doi.org/10.1007/s00170-004-
2227-z
Rosyidi, C.N., Irianto, D., & Toha, I.S. (2009). Prioritizing Key Characteristics. Journal of Advanced
Manufacturing Systems, 8(1), 57-70. https://doi.org/10.1142/S0219686709001675
Seyed-Hosseini, S-M., & Damghani, K.K. (2009). Fuzzy Containers Allocation Problem in Maritime
Terminal. Journal of Industrial Engineering and Management, 2(2), 323-336.
https://doi.org/10.3926/jiem.2009.v2n2.p323-336
Stella, H., & Alena, V. (2012). Application of Fuzzy Principles in Evaluating Quality of Manufacturing
Process. WSEAS Transactions on Power Systems, 2(7).
Teeravaraprug, J. (2008). Outsourcing and Vendor Selection Model Based On Taguchi Loss Function.
Songklanakarin Journal of Science and Technology, 30(4), 523-530.
Weber, C.A., Current, J.R., & Desai, A. (2000). Vendor: A Structured Approach to Vendor Selection and
Negotiation. Journal of Business Logistics, 21(1),135-167.Xi, X. & Qin, Q. (2013). Product Quality Evaluation System Based on AHP Fuzzy Comprehensive
Evaluation. Journal of Industrial Engineering and Management, 6(1), 356-366. https://doi.org/10.3926/jiem.685
Youn, B.D. (2005). Integrated Framework for Design Optimization Under Aleatory And/Or Epistemic
Uuncertainties Using Adaptive-Loop Method. Proceedings of DETC ’05, California.
Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8, 338-353. https://doi.org/10.1016/S0019-
9958(65)90241-X
Zimmermann, H.J. (1991). Fuzzy Set Theory and Its Applications (2nd ed.). Kluwer Academic Publishers.
https://doi.org/10.1007/978-94-015-7949-0

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