Application of FUCA Method for Multi-Criteria Decision Making in Mechanical Machining Processes

  • Duc Trung Do Hanoi University of Industry, Vietnam
Keywords: MCDM, FUCA method, Mechanical machining

Abstract

Multi-criteria decision making (MCDM) is a very useful tool to find the best solution among many solutions. For most MCDM methods, the data must be normalized. However, the data normalization method has a significant influence on the results of ranking solutions. Choosing the right data normalization method is sometimes complicated. In many MCDM methods, FUCA is known as the method without using normalize the data. However, the FUCA method has a small limitation. All publications that were applied this method have not mentioned this limitation. In this study, this limitation was overcome and then used for multi-criteria decision making in some cases in the mechanical processing field. The ranked results of the solutions when determined by the FUCA method are compared with those ones when using other MCDM methods. The sensitivity analysis was also performed. The results show that the FUCA method can be used for multi-criteria decision making in mechanical machining. It is also expected to be successful when applying in other fields. The works in the future were mentioned in the last section of this article as well.

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References

Aytekin, A. (2021). Comparative Analysis of the Normalization Techniques in the Context of MCDM Problems. Decision Making: Applications in Management and Engineering, 4(2), 1-25. https://doi.org/10.31181/dmame210402001a DOI: https://doi.org/10.31181/dmame210402001a

Baydas, M. (2022). The effect of pandemic conditions on financial success rankings of BIST SME industrial companies: a different evaluation with the help of comparison of special capabilities of MOORA, MABAC and FUCA methods. Business & Management Studies: An International Journal, 10(1), 245-260. https://doi.org/10.15295/bmij.v10i1.1997

Baydas, M. (2022). Comparison of the Performances of MCDM Methods under Uncertainty: An Analysis on Bist SME Industry Index. OPUS – Journal of Society

Research, 19(46), 308-326. https://doi.org/10.26466/opusjsr.1064280

Baydas, M., Elma, O.E., & Pamucar, D. (2022). Exploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial markets. Expert Systems with Applications, 197, 116775. https://doi.org/10.1016/j.eswa.2022.116755

Baydas, M., & Pamucar, D. (2022). Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data. Mathematics, 10(7), 1115. https://doi.org/10.3390/math10071115

Bobar, Z., Bozanic, D., Djuric, K., & Pamucar, D. (2020). Ranking and Assessment of the Efficiency of Social Media using the Fuzzy AHP-Z Number Model - Fuzzy MABAC. Acta Polytechnica Hungarica, 17(3), 43-70.

Bozanic, D., Milic, A., Tesic, D., Sałabun, W., & Pamucar, D. (2021). D numbers – fucom – fuzzy rafsi model for selecting the group of construction machines for enabling mobility. FACTA UNIVERSITATIS - Mechanical Engineering, 19(3), 447 – 471. https://doi.org/10.22190/FUME210318047B

Dimic Srđan, H., & Ljubojevic Srđan, D. (2019). Decision making model in forest road network management. Military Technical Courier, 67(1), 93-115. https://doi.org/10.5937/vojtehg67-18446

Ersoy, N. (2021). Selecting the Best Normalization Technique for ROV Method: Towards a Real Life Application. Gazi University Journal of Science, 34(2) 592-609. https://doi.org/10.35378/gujs.767525

Fernando, M.M.L, Escobedo, J.L.P., Azzaro-Pantel, C., Pibouleau, L., Domenech, S., & Aguilar-Lasserre, A. (2011). Selecting the best alternative based on a hybrid multiobjective GA-MCDM approach for new product development in the pharmaceutical industry. IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), https://ieeexplore.ieee.org/document/5949271

Krishnan, A.R. (2022). Past efforts in determining suitable normalization methods for multi-criteria decision-making: A short survey. Frontiers in Big Data, 5, 990699. https://doi.org/10.3389/fdata.2022.990699

Lakshmi, T.M, & Venkatesan, V.P. (2014). A Comparison of Various Normalization in Techniques for Order Performance by Similarity to Ideal Solution (TOPSIS). International Journal of Computing Algorithm, 3(3), 255-259. DOI: https://doi.org/10.20894/IJCOA.101.003.003.023

Lamba, M., Munjal, G., & Gigras, Y. (2022). A MCDM-based performance of classification algorithms in breast cancer prediction for imbalanced datasets. International Journal of Intelligent Engineering Informatics, 9(5), 425-454. https://doi.org/10.1504/IJIEI.2021.10044779

Le, H.A., Hoang, X.T., Trieu, Q.H., Pham, D.L., & Le, X. H. (2020). Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method. Applied scicences, 12(16), 8287. https://doi.org/10.3390/app12168287

Malkin, S. & Guo, C. (2008). Grinding technology: Theory and Applications of Machining with Abrasives (2nd Edition). New York: Industrial Press.

Marinescu, I.D., Hitchiner, M.P., Uhlmann, E., Rowe, W.B., & Inasaki, I. (2006). Handbook of machining with grinding wheels. CRC Press. https://doi.org/10.1201/b19462 DOI: https://doi.org/10.1201/9781420017649

Muhammad, L.J., Badi, I., Haruna, A.A., & Mohammed, I.A. (2021). Selecting the Best Municipal Solid Waste Management Techniques in Nigeria Using Multi Criteria Decision Making Techniques. Reports in Mechanical Engineering, 2(1), 180-189. https://doi.org/10.31181/rme2001021801b DOI: https://doi.org/10.31181/rme2001021801b

Nguyen, V.C, Nguyen, T.D, & Tien, D.H. (2021). Cutting Parameter Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under MQL Condition using TOPSIS and ANOVA Analysis. Engineering, Technology & Applied Science Research, 11(1), 6775-6780. https://doi.org/10.48084/etasr.4015

Ouattara, A., Pibouleau, L., Azzaro-Pantel, C., Domenech, S., Baudet, P., & Yao, B. (2022). Economic and environmental strategies for process design. Computers & Chemical Engineering, 36, 174-188. https://doi.org/10.1016/j.compchemeng.2011.09.016 DOI: https://doi.org/10.1016/j.compchemeng.2011.09.016

Palczewski, K., & Sałabun, W. (2019). Influence of various normalization methods in PROMETHEE II: an empirical study on the selection of the airport location. Procedia Computer Science, 159, 2051-2060. https://doi.org/10.1016/j.procs.2019.09.378

Pamucar, D., Behzad, M., Bozanic, D., & Behzad, M. (2021). Decision making to support sustainable energy policies corresponding to agriculture sector: Case study in Iran's Caspian Sea coastline. Journal of Cleaner Production, 292, 125302. https://doi.org/10.1016/j.jclepro.2020.125302

Singh, R., Dureja, J.S, Dogra, M., & Randhawa, J.S. (2019). Optimization of machining parameters under MQL turning of Ti-6Al-4V alloy with textured tool using multi-attribute decision-making methods. World Journal of Engineering, 16(5), 648–659. https://doi.org/10.1108/WJE-06-2019-0170

Stanujkic, D., Zavadskas, E.K., Karbasevic, D., Smarandache, F., Turskis, Z. (2017). The use of the PIvot Pairwise RElative Criteria Importance Assessment method for determining the weights of criteria. Romanian Journal of Economic Forecasting, 20(4), 116-133.

Trung, D.D. (2022). Development of data normalization methods for multi-criteria decision making: applying for MARCOS method. Manufacturing review, 9, 22. https://doi.org/10.1051/mfreview/2022019

Varatharajulu, M., Duraiselvam, M., Bhuvanesh Kumar, M., Jayaprakash, G., & Baskar, N. (2021). Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91. Journal of Magnesium and Alloys, 8(38), 1-18. https://doi.org/10.1016/j.jma.2021.05.006

Wen, Z., Liao, H., & Zavadskas, E.K. (2020). MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica, 31(4), 857-880. https://doi.org/10.15388/20-INFOR417

Zopounidis, C., & Doumpos, M. (2017). Multiple Criteria Decision Making - Applications in Management and Engineering. Springer. https://doi.org/10.1007/978-3-319-39292-9 DOI: https://doi.org/10.1007/978-3-319-39292-9

Published
2022-10-05
How to Cite
Do, D. T. (2022). Application of FUCA Method for Multi-Criteria Decision Making in Mechanical Machining Processes. Operational Research in Engineering Sciences: Theory and Applications. https://doi.org/10.31181/oresta051022061d
Section
Articles