Fuzzy Applications of FUCOM Method in Manufacturing Environment

Year 2020, , 189 - 195, 01.03.2020

Mehmet Alper Sofuoğlu

https://doi.org/10.2339/politeknik.586036

Cited By: 15

Abstract

Conventional manufacturing methods are limited in the
machining of newly developed high strength, precision / brittle and complex shaped parts. Non-conventional
manufacturing methods are required to machine such parts. Choosing the most
suitable manufacturing method for the part is a vital decision-making problem and the solution of this problem is very important for today's manufacturers. In
this study, three different Full Consistency Method (FUCOM) methods were
combined with fuzzy Technique for Order Preference by Similarity to Ideal
Solution method (fuzzy TOPSIS) and fuzzy weighted aggregated sum product
assessment (fuzzy WASPAS) techniques. In
order to test these developed methods, the selection of non-traditional
manufacturing methods from the literature was
taken as a case study. It is seen
that the model produced successful results.

Keywords

FUCOM, WASPAS, Nontraditional manufacturing method, fuzzy MCDM

References

• 1. Roberts, R. and Goodwin, P. (2002) Weight approximations in multi-attribute decision models. J. Multicrit. Decis. Anal.11, 291–303.
• 2. Solymosi, T. and Dompi, J. (1985) Method for determining the weights of criteria: The centralized weights. Eur. J. Oper. Res. 26, 35–41.
• 3. Cook,W.D. (2006) Distance-based and ad hoc consensus models in ordinal preference ranking. Eur. J. Oper. Res.172, 369–385.
• 4. Weber, M. (1993) Borcherding, K. Behavioral influences on weight judgments in multiattribute decision making. Eur. J. Oper. Res. 67, 1–12.
• 5. Zhu, G.N., Hu, J., Qi, J., Gu, C.C., Peng, J.H. (2015) An integrated AHP and VIKOR for design concept evaluation based on rough number. Adv. Eng. Inform. 29, 408–418.
• 6. Zavadskas, E.K.,Govindan, K., Antucheviciene, J.,Turskis, Z. (2016) Hybrid multiple criteria decision-making methods: A review of applications for sustainability issues. Econ. Res.-Ekonomska Istraživanja,29, 857–887.
• 7. Madić, M., Radovanović, M., Petković, D, (2015) Non-conventional machining processes selection using multi-objective optimization on the basis of ratio analysis method, Journal of Engineering Science and Technology, (10)11,1441-1452.
• 8. Khandekar, A. V., Chakraborty, S. (2016) Application of fuzzy axiomatic design principles for selection of non-traditional machining processes, International Journal of Advanced Manufacturing Technology, 83(1-4), 529-543.
• 9. Roy, M. K., Ray, A., Pradhan, B. B., (2017) Non-traditional machining process selection-an integrated approach, International Journal for Quality Research, 11(1), 71-94.
• 10. Boral, S., Chakraborty, S., (2016) A case-based reasoning approach for non-traditional machining processes selection. Advances in Production Engineering & Management, 11(4), 311-323.
• 11. Chen, S.J., and Hwang, C.L., (1992) Fuzzy Multiple Attribute Decision Making’ (Springer-Verlag, 1992)
• 12. Zadeh, L., (1975) The concept of a linguistic variable and its applications to approximate reasoning, Inform Sciences, Part I (No. 8), 199–249.
• 13. Carlsson, C., and Fullér, R., (1996) Fuzzy multiple criteria decision making: Recent developments, Fuzzy Set Syst, 1996, 78, (2),139-153.
• 14. Ribeiro, R.A., (1996) Fuzzy multiple attribute decision making: A review and new preference elicitation techniques’, Fuzzy Set Syst, 78, (2),155-181.
• 15. Triantaphyllou, E., and Lin, C.T.,(1996) Development and evaluation of five fuzzy multiattribute decision-making methods, Int J Approx Reason, 14, (4),281-310
• 16. Abdullah, L., (2013) Fuzzy Multi Criteria Decision Making and its Applications: A Brief Review of Category, Procedia -Social and Behavioral Sciences, 97,131-136.
• 17. Atanassov, K.T., (1986) Intuitionistic Fuzzy Sets, Fuzzy Set Syst, 20,87-96.
• 18. Yager, R.R., (1986) On The Theory of Bags, Int J Gen Syst, 13, (1),23-37.
• 19. Torra, V., (2010) Hesitant fuzzy sets, Int J Intell Syst, 25, (6),529-539.
• 20. Xu, Z., (2014) Hesitant Fuzzy Sets Theory (Springer, 2014).
• 21. Rodriguez, R.M., Martinez, L., and Herrera, F., (2012) Hesitant Fuzzy Linguistic Term Sets for Decision Making, Fuzzy Systems, IEEE Transactions on, 20, (1),109- 119.
• 22. Chen, S., and Hwang, C.L., (1992) Fuzzy Multiple Attribute Decision Making Methods and Applications (Springer- Verlag, 1992).
• 23. Ye, F., and Li, Y.N., (2014) An extended TOPSIS model based on the Possibility theory under fuzzy environment, Knowl-Based Syst,67, 263-269.
• 24. Kahraman, C., Çevik, S., Ates, N.Y., and Gülbay, M., (2007) Fuzzy multi-criteria evaluation of industrial roboticsystems, Computers and Industrial Engineering, 52, (4), 414-433.
• 25. Chen, C.B., and Wei, C.C., (1997) An approach for solving fuzzy MADM problems, International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 5, (4), 459-480.
• 26. Kannan, D., De Sousa Jabbour, A.B.L., and Jabbour, C.J.C., (2014) Selecting green suppliers based on GSCM practices: Using Fuzzy TOPSIS applied to a Brazilian electronics company, Eur J Oper Res, 233, (2), 432-447.
• 27. Wang, Y.J., (2014) The evaluation of financial performance for Taiwan container shipping companies by fuzzy TOPSIS, Applied Soft Computing Journal, 22, 28-35.
• 28. Chu, T.C., (2002) Facility location selection using fuzzy topsis under group decisions, International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 10, (6), 687-701.
• 29. Mandic, K., Delibasic, B., Knezevic, S., and Benkovic, S., (2014) Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods, Economic Modelling, 43, 30-37.
• 30. Zhang, G., and Lu, J., (2003) An Integrated Group Decision- Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria, Group Decision and Negotiation, 12, 501-515.
• 31. Tsaura, S.H., Chang, T.Y., and Yen, C.H., (2002) The evaluation of airline service quality by fuzzy MCDM, Tourism Management, 23, (2), 107-115.
• 32. Chen C.T., (2000) Extensions of the TOPSIS for Group Decision Making under Fuzzy Environment. Fuzzy Sets and Systems. 114, 1-9.
• 33. Turskis, Z. and Zavadskas, E. K. and Antucheviciene, J. and Kosareva, N., (2015) A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection. International Journal of Computers Communications & Control, 10(6), 873-888.
• 34. Pamucar, D., Stevic, Z., Sremac, S. (2018) A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM), Symmetry, 10(9), 393.
• 35. Kul Y. Şeker A., and Yurdakul M., (2014). Usage of fuzzy multi criteria decision making methods in selection of nontraditional manufacturing methods. Journal of the Faculty of Engineering and Architecture of Gazi University. 29/3, 589-603.