Aircraft Supplier Selection Process with Fuzzy Proximity Measure Method using Multiple Criteria Group Decision Making Analysis
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Aircraft Supplier Selection Process with Fuzzy Proximity Measure Method using Multiple Criteria Group Decision Making Analysis

Authors: C. Ardil

Abstract:

Being effective in every organizational activity has become necessary due to the escalating level of competition in all areas of corporate life. In the context of supply chain management, aircraft supplier selection is currently one of the most crucial activities. It is possible to choose the best aircraft supplier and deliver efficiency in terms of cost, quality, delivery time, economic status, and institutionalization if a systematic supplier selection approach is used. In this study, an effective multiple criteria decision-making methodology, proximity measure method (PMM), is used within a fuzzy environment based on the vague structure of the real working environment. The best appropriate aircraft suppliers are identified and ranked after the proposed multiple criteria decision making technique is used in a real-life scenario.

Keywords: Aircraft supplier selection, multiple criteria decision making, fuzzy sets, proximity measure method, Minkowski distance family function, Hausdorff distance function, PMM, MCDM

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[1] Ghodsypour, S. H., O’Brien C. (1998). A decision support system for supplier selection using an integrated analytic hierarchy process and linearprogramming.International Journal of Production Economics, 56-57, 199-212.
[2] Weber, C. A., Current, J. R., , Benton, W. C. (1991). Vender selection criteria and methods. European Journal of Operational Research, 50, 2-18.
[3] Degraeve, Z., Labro, E., Roodhooft, F. (2000). An evaluation of supplier selection methods from a total cost of ownership perspective. European Journal of Operational Research, 125(1), 34-59.
[4] De Boer, L., Labro, E., Morlacchi, P. (2001). A review of methods supporting supplier selection European Journal of Purchasing . & Supply Management, 7, 75-89.
[5] Ho, W., Xu, X. D., Prasanta K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202, 16-24.
[6] Sanayei, A., Mousavi, S. F., Yazdankhah, A. (2010). Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37, 24-30.
[7] Chen,C. T., Lin,C. T., Huang, S. F.(2006). A fuzzy approach for supplier evaluation and selection in supply chain management, International Journal of Production Economics, vol. 102(2), 289–301.
[8] Min, H. (1994). International supplier selection: a multi-attribute utility approach, International Journal of Physical Distribution and Logistics Management, vol. 24(5), 24–33.
[9] Boran, FE., Genç, S., Kurt, M., Akay, D., (2009). A Multi-Criteria Intuitionistic Fuzzy Group Decision Making For Supplier Selection With TOPSIS Method”, Expert Systems with Applications, 36(8), pp.11363-11368, 2009.
[10] Izadikhah, M. (2012). Group Decision Making Process for Supplier Selection with TOPSIS Method under Interval-Valued Intuitionistic Fuzzy Numbers, Advances in Fuzzy Systems, vol. 2012, Article ID 407942.
[11] Saaty, T. L. (1990). How to make a decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26.
[12] Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.
[13] Buckley,J.J. (1985). Fuzzy hierarchical analysis, Fuzzy Sets and Systems, 17, 233–247.
[14] Dyer, J.S. (2016). Multiattribute Utility Theory (MAUT). In: Greco, S., Ehrgott, M., Figueira, J. (eds) Multiple Criteria Decision Analysis. International Series in Operations Research & Management Science, vol 233. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3094-4_8.
[15] Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
[16] Chu, T.C. (2002. Facility location selection using fuzzy TOPSIS under group decisions, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 10 No. 6, pp. 687-701.
[17] Opricovic, S. (2007). A fuzzy compromise solution for multicriteria problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(3), 363–380.
[18] Opricovic, S., Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.
[19] Roy, B. (1991). The outranking approach and the foundation of ELECTRE methods. Theory and Decision, 31(1), 49–73.
[20] Fei, L., Xia, J., Feng, Y., Liu, L. (2019) An ELECTRE-Based Multiple Criteria Decision Making Method for Supplier Selection Using Dempster-Shafer Theory. IEEE Access, 7, 84701-84716.
[21] Brans JP., Mareschal B. (2005). Promethee Methods. In: Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science, vol 78, pp 163-186. Springer, New York, NY. https://doi.org/10.1007/0-387-23081-5_5.
[22] Brans, J., Ph. Vincke. (1985). A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management Science, 31(6), 647-656.
[23] Brans, J.P., Macharis, C., Kunsch, P.L., Chevalier, A., Schwaninger, M., (1998). Combining multicriteria decision aid and system dynamics for the control of socio-economic processes. An iterative real-time procedure. European Journal of Operational Research 109, 428-441.
[24] Brans, J.P., Vincke, Ph., Mareschal, B., (1986). How to select and how to rank projects: the PROMETHEE method. European Journal of Operational Research, 24, 228-238.
[25] Taherdoost, H., Madanchian, M. (2023). Multi-Criteria Decision Making (MCDM) Methods and Concepts. Encyclopedia, 3(1), 77–87.
[26] Ardil, C. (2023). Aircraft Supplier Selection using Multiple Criteria Group Decision Making Process with Proximity Measure Method for Determinate Fuzzy Set Ranking Analysis. International Journal of Industrial and Systems Engineering, 17(3), 127 - 135.
[27] Ardil, C. (2023). Determinate Fuzzy Set Ranking Analysis for Combat Aircraft Selection with Multiple Criteria Group Decision Making. International Journal of Computer and Information Engineering, 17(3), 272 - 279.
[28] Ardil, C. (2019). Fighter Aircraft Selection Using Technique for Order Preference by Similarity to Ideal Solution with Multiple Criteria Decision Making Analysis. International Journal of Transport and Vehicle Engineering, 13(10), 649 - 657.
[29] Ardil, C. (2019). Aircraft Selection Using Multiple Criteria Decision Making Analysis Method with Different Data Normalization Techniques. International Journal of Industrial and Systems Engineering, 13(12), 744 - 756.
[30] Ardil, C. (2019). Military Fighter Aircraft Selection Using Multiplicative Multiple Criteria Decision Making Analysis Method. International Journal of Mathematical and Computational Sciences, 13(9), 184 - 193.
[31] Ardil, C. (2020). A Comparative Analysis of Multiple Criteria Decision Making Analysis Methods for Strategic, Tactical, and Operational Decisions in Military Fighter Aircraft Selection. International Journal of Aerospace and Mechanical Engineering, 14(7), 275 - 288.
[32] Ardil, C. (2020). Aircraft Selection Process Using Preference Analysis for Reference Ideal Solution (PARIS). International Journal of Aerospace and Mechanical Engineering, 14(3), 80 - 93.
[33] Ardil, C. (2020). Regional Aircraft Selection Using Preference Analysis for Reference Ideal Solution (PARIS). International Journal of Transport and Vehicle Engineering, 14(9), 378 - 388.
[34] Ardil, C. (2020). Trainer Aircraft Selection Using Preference Analysis for Reference Ideal Solution (PARIS). International Journal of Aerospace and Mechanical Engineering, 14(5), 195 - 209.
[35] Ardil, C. (2021). Advanced Jet Trainer and Light Attack Aircraft Selection Using Composite Programming in Multiple Criteria Decision Making Analysis Method. International Journal of Aerospace and Mechanical Engineering, 15(12), 486 - 491.
[36] Ardil, C. (2021). Airline Quality Rating Using PARIS and TOPSIS in Multiple Criteria Decision Making Analysis. International Journal of Industrial and Systems Engineering, 15(12), 516 - 523.
[37] Ardil, C. (2021). Comparison of Composite Programming and Compromise Programming for Aircraft Selection Problem Using Multiple Criteria Decision Making Analysis Method. International Journal of Aerospace and Mechanical Engineering, 15(11), 479 - 485.
[38] Ardil, C. (2021). Fighter Aircraft Evaluation and Selection Process Based on Triangular Fuzzy Numbers in Multiple Criteria Decision Making Analysis Using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). International Journal of Computer and Systems Engineering, 15(12), 402 - 408.
[39] Ardil, C. (2021). Military Combat Aircraft Selection Using Trapezoidal Fuzzy Numbers with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). International Journal of Computer and Information Engineering, 15(12), 630 - 635.
[40] Ardil, C. (2021). Freighter Aircraft Selection Using Entropic Programming for Multiple Criteria Decision Making Analysis. International Journal of Mathematical and Computational Sciences, 15(12), 125 - 132.
[41] Ardil, C. (2021). Neutrosophic Multiple Criteria Decision Making Analysis Method for Selecting Stealth Fighter Aircraft. International Journal of Aerospace and Mechanical Engineering, 15(10), 459 - 463.
[42] Ardil, C. (2022). Aircraft Selection Problem Using Decision Uncertainty Distance in Fuzzy Multiple Criteria Decision Making Analysis. International Journal of Mechanical and Industrial Engineering, 16(3), 62 - 69.
[43] Ardil, C. (2022). Aircraft Selection Using Preference Optimization Programming (POP).International Journal of Aerospace and Mechanical Engineering, 16(11), 292 - 297.
[44] Ardil, C. (2022). Fighter Aircraft Selection Using Fuzzy Preference Optimization Programming (POP). International Journal of Aerospace and Mechanical Engineering, 16(10), 279 - 290.
[45] Ardil, C. (2022). Fighter Aircraft Selection Using Neutrosophic Multiple Criteria Decision Making Analysis. International Journal of Computer and Systems Engineering, 16(1), 5 - 9.
[46] Ardil, C. (2022). Military Attack Helicopter Selection Using Distance Function Measures in Multiple Criteria Decision Making Analysis. International Journal of Aerospace and Mechanical Engineering, 16(2), 20 - 27.
[47] Ardil, C. (2022). Multiple Criteria Decision Making for Turkish Air Force Stealth Fighter Aircraft Selection. International Journal of Aerospace and Mechanical Engineering, 16(12), 369 - 374.
[48] Ardil, C. (2022). Vague Multiple Criteria Decision Making Analysis Method for Fighter Aircraft Selection. International Journal of Aerospace and Mechanical Engineering, 16(5),133-142.
[49] Ardil, C. (2022).Fuzzy Uncertainty Theory for Stealth Fighter Aircraft Selection in Entropic Fuzzy TOPSIS Decision Analysis Process. International Journal of Aerospace and Mechanical Engineering, 16(4), 93 - 102.
[50] Ardil, C. (2023). Fuzzy Multiple Criteria Decision Making for Unmanned Combat Aircraft Selection Using Proximity Measure Method. International Journal of Computer and Information Engineering, 17(3), 193 - 200.
[51] Ardil, C. (2023). Unmanned Combat Aircraft Selection using Fuzzy Proximity Measure Method in Multiple Criteria Group Decision Making. International Journal of Computer and Systems Engineering, 17(3), 238 - 245.
[52] Ardil, C. (2023). Using the PARIS Method for Multiple Criteria Decision Making in Unmanned Combat Aircraft Evaluation and Selection. International Journal of Aerospace and Mechanical Engineering, 17(3), 93 - 103.
[53] Ardil, C. , Pashaev, A. , Sadiqov, R. , Abdullayev, P. (2019). Multiple Criteria Decision Making Analysis for Selecting and Evaluating Fighter Aircraft. International Journal of Transport and Vehicle Engineering, 13(11), 683 - 694.
[54] Zadeh, L. A. (1965). Fuzzy sets. Inf. Control. 8(3), 338–353.
[55] Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3), 199–249.
[56] Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-II. Inf. Sci. 8(4), 301–357.
[57] Zadeh, L. A. (1975).The concept of a linguistic variable and its application to approximate reasoning-III. Inf. Sci. 9(1), 43–80.
[58] Atanassov, K. (1986).Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96.
[59] Awasthi, A., Chauhan, S.S., Omrani, H. (2011). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Syst. Appl., 38, 12270-12280.
[60] Ecer, F., Pamucar, D. (2021).MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services. Appl. Sof Comput. 104, 107199.
[61] Verma, R. (2021). On intuitionistic fuzzy order-alpha divergence and entropy measures with MABAC method for multiple attribute group decision-making. J. Intell. Fuzzy. Syst. Appl. Eng. Technol. 40(1), 1191–1217.
[62] Ilbahar, E., Kahraman, C., Cebi, S. (2022). Risk assessment of renewable energy investments: A modifed failure mode and efect analysis based on prospect theory and intuitionistic fuzzy AHP. Energy 239, 121907.
[63] Verma, R. , Merig, J. M. (2020). A new decision making method using interval-valued intuitionistic fuzzy cosine similarity measure based on the weighted reduced intuitionistic fuzzy sets. Informatica 31(2), 399–433.
[64] Wang, Z., Xiao, F. , Ding, (2022).W. Interval-valued intuitionistic fuzzy Jenson–Shannon divergence and its application in multi-attribute decision making. Appl. Intell. 1–17.
[65] Verma, R. , Merigó, J. M. (2021). On Sharma-Mittal’s entropy under intuitionistic fuzzy environment. Cybern. Syst. 52(6), 498–521.
[66] Zhao, M., Wei, G. , Wei, C. (2021). Extended CPT-TODIM method for interval-valued intuitionistic fuzzy MAGDM and its application to urban ecological risk assessment. J. Intell. Fuzzy Syst. 40(3), 4091–4106.
[67] Liu, P., Pan, Q., Xu, H. (2021). Multi-attributive border approximation area comparison (MABAC) method based on normal q-rung orthopair fuzzy environment. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 5, 40.
[68] Atanassov, K. , Gargov, G. (1989).Interval-valued intuitionistic fuzzy sets. Fuzzy Syst. 31(3), 343–349.
[69] Hajiagha, S. H. R., Mahdiraji, H. A., Hashemi, S. S. , Zavadskas, E. K. (2015).Evolving a linear programming technique for MAGDM problems with interval valued intuitionistic fuzzy information. Expert Syst. Appl. 42(23), 9318–9325.
[70] You, P., Liu, X. H. , Sun, J. B. (2021).A multi-attribute group decision making method considering both the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environment. Inf. Sci. 104, 107187.
[71] Ye, F. (2010).An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Syst. Appl. 37(10), 7050–7055.
[72] Chen, X., Suo, C. F. , Li, Y. G. (2021). Distance measures on intuitionistic hesitant fuzzy set and its application in decision-making. Comput. Appl. Math. 40(3), 63–84.
[73] Hou, X. Q. et al. (2016).Group decision-making of air combat training accuracy assessment based on interval-valued intuitionist fuzzy set. Syst. Eng. Electron. 38(12), 2785–2789.
[74] Liu, Y., Jiang, W. (2020).A new distance measure of interval-valued intuitionistic fuzzy sets and its application in decision making. Sof. Comput. 24(9), 6987–7003.
[75] Garg, H., Kumar, K. (2020).A novel exponential distance and its based TOPSIS method for interval-valued intuitionistic fuzzy sets using connection number of SPA theory. Artif. Intell. Rev 53(1), 595–624.
[76] Zhang, Z. M. , Chen, S. M. (2021).Optimization-based group decision making using interval-valued intuitionistic fuzzy preference relations. Inf. Sci. 561, 352–370.
[77] Atanassov, K. (1994).Operator over interval-valued intuitionistic fuzzy sets. Fuzzy Syst. 64(2), 159–174.
[78] Xu, Z. S. ,Yager, R. R. (2006).Some geometric aggregation operators based on intuitionistic fuzzy sets. Int. J. Gen. Syst. 35(4), 417–433.
[79] Xu, Z. S. , Chen, J. (2007).An approach to group decision making based on interval–valued intuitionistic judgment matrices. Syst. Eng. Theory Pract. 27(4), 126–133.
[80] Kong, D. P. et al. (2019).A decision variable-based combinatorial optimization approach for interval-valued intuitionistic fuzzy MAGDM. Inf. Sci. 484(5), 197–218.
[81] Yao, R. P. (2019).An Approach to variable weight group decision making based on the improved score function of interval-valued intuitionistic sets. Stat. Decis. 35(11), 36–38.
[82] Xu, Z. S. (2007).Method for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control Decis. 22(2), 215–219.
[83] Da, Q. , Liu, X. W. (1999).Interval number linear programming and its satisfactory solution. Syst. Eng. Teory Pract. 19(4), 3–7.
[84] Liu, H. C., Chen, X. Q., Duan, C. Y. , Wang, Y. M. (2019). Failure Mode and Effect Analysis Using Multi Criteria Decision Making Methods; A Systematic Literature Review. Computers and Industrial Engineering, 135, 881-897.
[85] Chen, M., Tzeng, G. (2004). Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Math. Comput. Model., 40, 1473-1490.
[86] Gupta, R., Kumar, S. (2022). Intuitionistic fuzzy scale-invariant entropy with correlation coefficients-based VIKOR approach for multi-criteria decision-making. Granular Computing, 7, 77-93.
[87] Tuğrul, F. (2022). An Approach Utilizing The Intuitionistic Fuzzy TOPSIS Method to Unmanned Air Vehicle Selection.Ikonion Journal of Mathematics 4(2) 32-41.
[88] Altuntas,G.,Yildirim, B.F. (2022).Logistics specialist selection with intuitionistic fuzzy TOPSIS method, International Journal of Logistics Systems and Management, vol. 42(1), 1-34.
[89] Yao, R., Guo, H. (2022). A multiattribute group decision-making method based on a new aggregation operator and the means and variances of interval-valued intuitionistic fuzzy values. Sci Rep 12, 22525.
[90] Wang, Y., Lei, Y.J. (2007). A Technique for Constructing intuitionistic Fuzzy Entropy. J. Control Decis. 12, 1390–1394.
[91] Fu, S., Xiao, Yz., Zhou, Hj. (2022). Interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering risk preference of decision-makers and its application. Sci Rep 12, 11597.
[92] Liu, P., Gao, H. (2018), An overview of intuitionistic linguistic fuzzy information aggregations and applications. Marine Economics and Management, Vol. 1 No. 1,55-78.
[93] Yager, RR. (2013. Pythagorean fuzzy subsets. Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) 57–61 6.
[94] Yager, R. R. (2013). Pythagorean membership grades in multi-criteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965.
[95] Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222– 1230.
[96] Tian, X., Niu, M., Zhang, W., Li, L., Herrera-Viedma, E. (2021). A novel TODIM based on prospect theory to select green supplier with q-rung orthopair fuzzy set. Technological and Economic Development of Economy, 27(2), 284-310.
[97] Cuong, B. C., Kreinovich, V. (2013). Picture Fuzzy Sets - a new concept for computational intelligence problems. Departmental Technical Reports (CS). 809. In Proceedings of the Third World Congress on Information and Communication Technologies WICT'2013, Hanoi, Vietnam, December 15-18, 2013, pp. 1-6.
[98] Cuong, B. C. (2014). Picture Fuzzy Sets. Journal of Computer Science and Cybernetics, V.30, N.4 (2014), 409–420.
[99] Gündogdu, FK, Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. J Intell Fuzzy Syst 36(1):337–352.
[100] Mahmood, T.; Ullah, K.; Khan, Q.; Jan, N. An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Comput Appl. 2018, 1–13.
[101] Ullah, K., Mahmood, T., Jan, N. (2018). Similarity Measures for T-Spherical Fuzzy Sets with Applications in Pattern Recognition. Symmetry, 10(6), 193.
[102] Smarandache, F. (2003). A unifying field in logics neutrosophic logic. Neutrosophy, neutrosophic set, neutrosophic probability. (3rd ed.). Xiquan, Phoenix: American Research Press.
[103] Smarandache, F. (2003).Neutrosophic Logic - Generalization of the Intuitionistic Fuzzy Logic. https://arxiv.org/abs/math/0303009