SELECTION METHOD OF GRINDING MACHINE AND AIR CLASSIFIER IN GRINDING-CLASSIFICATION PROCESS BY USING FSFDMW-TOPSIS
DOI:
https://doi.org/10.51630/ijes.v6i3.192Keywords:
Decision Makers` Weight, fuzzy score function, FSFDMW-TOPSIS method, grinding machine, air classifier, grinding-classification processAbstract
Type selection of grinding machines and air classifiers is a critical issue in dry grinding–classification process design, particularly under uncertain environments where statistical data are unavailable and expert judgments dominate decision making. This study proposes a fuzzy group decision-making framework integrating fuzzy equivalence clustering, fuzzy score function with decision makers’ weights (FSFDMW), and TOPSIS to enhance selection reliability. First, main criteria are identified using fuzzy equivalence clustering. Then, an n-dimensional fuzzy environment is constructed to determine the weights of decision makers and criteria. Finally, a TOPSIS procedure based on fuzzy score functions is applied to rank alternatives. Application to the dental gypsum grinding–classification process shows that the impact mill achieves the highest priority value (0.742), while the MS type air classifier obtains the highest priority value (0.96417). The proposed framework improves decision accuracy while maintaining computational simplicity.
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Y. M. Wang, T. M. S. Elhag, “Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment”, Expert systems with applications 31, pp. 309-319, (2006).
C. T. Chen, “Extension of the TOPSIS for set decision making under fuzzy environment”. Fuzzy Sets Syst 114, pp.1-9, (2000).
Jahanshahloo GR, Hosseinzadeh LF, Izadikhah M. Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl Math Comput 2006;181(2):1544–51.
Wang TC, Chang TH. Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Exp Syst Appl 2007;33(4):870–80.
C. T. Chu, “Selecting plant location via a fuzzy TOPSIS approach”, Int J Adv Manuf Technol 20: pp.859-864, (2002).
Z. Gligoric, C. Beljic, V. Simeunovic, “Shaft location selection at deep multiple orebody deposit by using fuzzy TOPSIS method and network optimization“, Expert systems with applications (37) 2, pp.1408-1418, (2010).
Doukas H, Karakosta C, Psarras J. A linguistic TOPSIS model to evaluate the sustainability of renewable energy options. Int J Energy Issues 2009;32 (1):102–18.
Ghodsipour SH. Analytical hierarchy process. Tehran: Amirkabir University of Technology Publication; 2009.
Safari M, Ataei M, Khalokakaie R, Karamozian M. Mineral processing plant location using the analytic hierarchy process-a cas study: the Sangan iron ore mine. Min Sci Technol 2010;20(5): 691–5.
Ataei M, Shahsavany H, Mikaeil R. Monte Carlo Analytical Hierarchy Process (MAHP) approach to selection of optimum mining method. Int J Min Sci Technol 2013;23(4):573–8.
Rahimdel MJ, Ataei M. Application of analytical hierarchy process to selection of primary crusher. Int J Min Sci Technol 2014;24(4):519–23.
Karimnia H, Bagloo H. Optimum mining method selection using fuzzy analytical hierarchy process-Qapiliq salt mine, Iran. Int J Min Sci Technol 2015;25(2):225–30.
Moham mad A. Al-Anbari, Muhannad Y. Thameer, Nadhir Al-Ansari and Sven Knutsson, Ranking Landfill Sites in Najat Governoratelrag Using AHP and Fuzzy Topsis Methods, Journal of Givil Engineering and Architecture. 10(2016) 815-821
Alizadeh Shahab, Salari Rad Mohammad Mehdi, Bazzazi Abbas Aghajani, Alunite processing method selection using the AHP and TOPSIS approaches under fuzzy environment, International Journal of Mining Science and Technology xxx (2016) xxx–xxx.
Lin L, Yuan XH, Xia ZQ. Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets. J Comp Syst Sci 2007;73(1):84–8.
Liu HW, Wang GJ. Multi-criteria decision-making methods based on intuitionistic fuzzy sets. Euro J Operat Res 2007;179(1): 220–33.
Yue Z. Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Exp Syst Appl 2012;39(7):6343–50.
Chai J, Liu JNK, Xu Z. A rule-based set decision model for warehouse evaluation under interval-valued intuitionistic fuzzy environments. Exp Syst Appl 2013;40(6):1959–70.
Z.S. Xu, X.L. Zhang, Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowledge-Based Syst. 52 (2013), 53–64.
Zhang H, Yu L. MADM method based on cross-entropy and extended TOPSIS with interval-valued intuitionistic fuzzy sets. Knowl-Based Syst 2012;30:115–20.
Deepa Joshi, Sanjay Kumar, Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making. Egyptian Informatics Journal (2014) 15, 97–104.
X.D. Liu, J.J. Zhu, S.T. Zhang, J.J. Hao, G.D. Liu, Integrating LINMA and TOPSIS methods for hesitant fuzzy multiple attribute decision making. J. Intell. Fuzzy Syst. 28(1) (2015), 257–269.
Z.M. Zhang, Hesitant Fuzzy Multi-Criteria Set Decision Making with Unknown Weight Information, Int. J. Fuzzy Syst. 19(3) (2017), 615–636.
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