Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-41482 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 21 article 1079 pages: 361-373

Do Duc Trung*
Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam

Tran Ngoc Tan
Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam

When performing the multi-criteria decision making to choose the best solution, if some solutions are removed from the list of solutions or some solutions are added to the list of solutions, the decision making must be re-performed from the begining. This study proposes a new method to remove this limitation. The combination of the DOE (Design Of Experimental) method and PIV (Proximity Indexed Value) method is proposed in this paper. This combination is used to build the relationship between the scores of the solutions and the criteria. When the list of solution to be ranked has been removed or have been added some solutions, the ranking of some solutions only needs to use this relationship without having to recalculate from the beginning. Four different examples were applied to evaluate the effectiveness of the proposed method. The obtained results show that the proposed method ensures the required accuracy as well as its outstanding advantages. The limitations of the proposed method that need to be overcome are also pointed out at the end of this paper.

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1.      Zizovic, M., Pamucar, D., Albijanic, M., Chatterjee, P., Pribicevic, I. (2020). Eliminating Rank Reversal Problem Using a New Multi-Attribute Model—The RAFSI Method. Methematics, vol. 8, no. 1015, 1-16, DOI: 10.3390/math8061015

2.      Tien, D. H., Trung, D. D., Thien, N. V. (2022). Comparison of multi-criteria decision making methods using the same data standardization method. Strojnícky časopis - Journal of Mechanical Engineering, vol. 72, no. 2, 2022, 57-72, DOI: 10.2478/scjme-2022-0016

3.      Dua, T. V. (2022). Application of the Collaborative unbiased rank list integration method to select the materials. Applied Engineering Letters, vol.7, no.4, 133-142, DOI: 10.18485/aeletters.2022.7.4.1

4.      Ardil, C. (2020). Aircraft Selection Process Using Preference Analysis for Reference Ideal Solution (PARIS). International Journal of Aerospace and Mechanical Engineering, vol. 14, no. 3, 89-90.

5.      Mufazzal, S., Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, vol. 119, 427–438, DOI: 10.1016/j.cie.2018.03.045

6.      Mufazzal, S., Muzakkir, S. (2018). A New Multi-Criterion Decision Making (MCDM) Method Based on Proximity Indexed Value for Minimizing Rank Reversals. Computers & Industrial Engineering, vol. 2018, 1-39, 2018, DOI: 10.1016/j.cie.2018.03.045

7.      Yu, Y., Wu, S., Yu, J., Chen, H., Zeng, Q., Xu, Y., Ding, H. (2022). An integrated MCDM framework based on interval 2-tuple linguistic: A case of offshore wind farm site selection in China. Process Safety and Environmental Protection, vol. 164, 613-628, DOI: 10.1016/j.psep.2022.06.041

8.      Khan, N. Z., Ansari, T. S. A., Siddiquee, A. N., Khan, Z. A. (2019). Selection of E‑learning websites using a novel Proximity Indexed Value (PIV) MCDM method. Journal of Computers in Education, 6, 241-256, DOI: 10.1007/s40692-019-00135-7

9.      Wakeel, S., Bingol, S., Bashir, M. N., Ahmad, S. (2020). Selection of sustainable material for the manufacturing of complex automotive products using a new hybrid Goal Programming Model for Best Worst Method–Proximity Indexed Value method. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 235, no. 2, 1-15, DOI: 10.1177/1464420720966347

10.   Wakeel, S., Bingol, S., Ahmad, S., Bashir, M. N., Emamat, M. S. M. M., Ding, Z., Fayaz, H. (2021). A New Hybrid LGPMBWM-PIV Method for Automotive Material Selection. Informatica, vol. 45, 105–115, DOI: 10.31449/inf.v45i1.3246

11.   Ulutas, A., Karakoy, C. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, vol. 5, no. 4, 49-69, DOI: 10.18559/ebr.2019.4.3

12.   Raigar, J., Sharma, V. S., Srivastava, S., Chand, R., Singh, J. (2020). A decision support system for the selection of an additive manufacturing process using a new hybrid MCDM technique. Sadhana, vol. 45, no. 101, 1-14, DOI: 10.1007/s12046-020-01338-w

13.   Trung, D. D. (2021). A combination method for multi-criteria decision making problem in turning process. Manufacturing review, vol. 8, no. 26, 1-17, DOI: 10.1051/mfreview/2021024

14.   Trung, D. D. (2021). Application of TOPSIS an PIV Methods for Multi - Criteria Decision Making in Hard Turning Process. Journal of Machine Engineering, vol. 21, no. 4, 57–71, DOI: 10.36897/jme/142599

15.   Trung, D. D. (2021). The Combination of Taguchi – Entropy – WASPAS - PIV Methods for Multi-Criteria Decision Making when External Cylindrical Grinding of 65G Steel. Journal of Machine Engineering, vol. 21, no. 4, 90–105, DOI: 10.36897/jme/144260

16.   Goswam, S. S., Mohanty, S. K., Behera, D. K. (2022). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool, Materialstoday: Proceeding, vol. 52, 1153-1160, DOI: 10.1016/j.matpr.2021.11.019

17.   Ersoy, N. (2021). Application of the PIV method in the presence of negative data: an empirical example from a real-world case. Hitit Journal of Social Sciences, vol. 14, no. 2, 318-337, DOI: 10.17218/hititsbd.974522

18.   Ersoy, N. (2022). Comparative analysis of MCDM methods for the assessment of ICT development in G7 countries. Kafkas Universitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi, vol. 25, 55-73, DOI: 10.36543/kauiibfd.2022.003

19.   Ulutas, A., Balo, F., Sua, L., Demir, E., Topal, A., Jakovljevic, V. (2021). A new integrated grey MCDM model: Case of warehouse location selection. Facta Universitatis, Series: Mechanical Engineeringvol. 19, no. 3, 515-535, DOI: 10.22190/FUME210424060U

20.   Ulutas, A., Karakus, C. B. (2021). Location selection for a textile manufacturing facility with GIS based on hybrid MCDM approach. Industria textilas, vol. 72, no. 2, 126-132, DOI: 10.35530/IT.072.02.1736

21.   Soni, P. K. (2021). Integrated Multi-Criteria Decision-Making Methods for Selection of Supply Chain Partner for Supply Chain Management. International Journal for Research in Applied Science & Engineering Technology, vol. 9, no. 12, 952-957.

22.   Shahid, M., Karimi, M. N. (2019). Multi-criteria decision-making approach for finding optimal energy efficient bulding model. Journal of Emerging Technologies and Innovative Research, vol. 6, no. 4, 175-183.

23.   Yu, Y., Wu, S., Yu, J., Chen, H., Zeng, Q., Xu, Y., Ding, H. (2022). An integrated MCDM framework based on interval 2-tuple linguistic: A case of offshore wind farm site selection in China. Process Safety and Environmental Protection, vol. 164, 613-628, DOI: 10.1016/j.psep.2022.06.041

24.   Wang, P., Zhu, Z., Huang, S. (2017). The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems. Journal of Intelligent Manufacturing, vol. 28, 229–243, DOI: 10.1007/s10845-014-0973-9

25.   Chattopadhyay, R., Das, P. P., Chakraborty, S. (2022). Development of a rough-MABAC-DoE-Based metamodel for supplier selection in an iron and steel industry. Operational Research in Engineering Sciences: Theory and Applications, vol. 5, no. 1, 20-40, DOI: 10.31181/oresta190222046c

26.   Trung, D. D. (2021). Application of EDAS, MARCOS, TOPSIS, MOORA and PIV Methods for Multi-Criteria Decision Making in milling process. Strojnícky časopis - Journal of Mechanical Engineering, vol. 71, no. 2, 69-48, DOI: 10.2478/scjme-2021-0019

27.   Trung, D. D. (2021). Influence of Cutting Parameters on Surface Roughness in Grinding of 65G Steel. Tribology in Industry, vol. 43, no. 1, 167-176, DOI: 10.24874/ti.1009.11.20.01

28.   Dean, A., Voss, D., Draguljic, D. (2007). Design and Analysis of Experiments - Second Edition, Springer.

29.   Trung, D. D. (2022). Application of FUCA method for multi-criteria decision making in mechanical machining process. Operational Research in Engineering Sciences: Theory and Applications, vol. 5, no. 3, 131-152, DOI: 10.31181/oresta051022061d

30.   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, vol. 17, no. 3, 43-70

31.   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, vol. 12, no. 16, 8287, DOI: 10.3390/app12168287

32.   Badi, I., Muhammad, L. J., Abubakar, M., Bakır, M. (2022). Measuring Sustainability Performance Indicators Using FUCOM-MARCOS Methods. Operational Research in Engineering Sciences: Theory and Applications, vol. 5, no. 2, 99-116, DOI: 10.31181/oresta040722060b

33.   Wen, Z., Liao, H., Zavadskas, E. K. (2020). MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica, vol. 31, no. 4, 857–880, DOI: 10.15388/20-INFOR417