Comparison of Fuzzy and Crisp Versions of an AHP and TOPSIS Model for Nontraditional Manufacturing Process Ranking Decision

Yurdakul, Mustafa; İç, Yusuf Tansel
June 2019
Journal of Advanced Manufacturing Systems;Jun2019, Vol. 18 Issue 2, p167
Academic Journal
Nontraditional manufacturing processes (NTMPs) are especially preferred when it is necessary to machine very small and delicate parts, obtain complex shapes or process very hard and high strength materials. New NTMPs are developed continually and the total number of NTMPs being used in the machining industry is increasing so that ranking and selection of the most proper NTMP requires multi-level and systematic models. [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] developed such an NTMP ranking model. The developed NTMP ranking model in [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] had a two-level structure and used crisp Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) together to rank feasible NTMPs. This study aims to replace crisp (nonfuzzy) versions of the AHP and TOPSIS with the fuzzy ones. Application of the fuzzy NTMP ranking model is illustrated and its results are compared with the ones obtained in [M. Yurdakul and C. Cogun, Development of a multi-attribute selection procedure for non-traditional machining processes, Proc. Inst. Mech. Eng. J. Eng. Manuf.217 (2003) 993–1009] to evaluate the significance of the differences in ranking results. The comparisons show that using fuzzy AHP and TOPSIS approaches instead of the crisp ones in the ranking model provided considerable ranking differences. The fuzzy NTMP ranking model is studied furthermore in the paper by updating the NTMP list and fine-tuning fuzzy weights of the pertinent attributes.


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