Vague Multiple Criteria Decision Making Analysis Method for Fighter Aircraft Selection
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Vague Multiple Criteria Decision Making Analysis Method for Fighter Aircraft Selection

Authors: C. Ardil

Abstract:

Fighter aircraft selection is one of the most critical strategies for defense multiple criteria decision-making analysis to increase the decisive power of air defense and its superior power in the defense strategy. Vague set theory is an adequate approach for modeling vagueness, uncertainty, and imprecision in decision-making problems. This study integrates vague set theory and the technique for order of preference by similarity to ideal solution (TOPSIS) to support fighter aircraft selection. The proposed method is applied in the selection of fighter aircraft for the Air Force. In the proposed approach, the ratings of alternatives and the importance weights of criteria for fighter aircraft selection are represented by the vague set theory. Finally, an illustrative example for fighter aircraft selection is given to demonstrate the applicability and effectiveness of the proposed approach. The fighter aircraft candidates were selected under six criteria including costability, payloadability, maneuverability, speedability, stealthility, and survivability. Analysis results show that the best fighter aircraft is selected with the highest closeness coefficient value. The proposed method can also be applied to solve other multiple criteria decision analysis problems. 

Keywords: fighter aircraft selection, vague set theory, fuzzy set theory, neutrosophic set theory, multiple criteria decision making analysis, MCDMA, TOPSIS

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 546

References:


[1] Ardil, C., Bilgen, S. (2017). Online Performance Tracking. SocioEconomic Challenges, 1(3), 58-72. ISSN (print) – 2520-6621.
[2] Ardil, C. (2018) Multidimensional Performance Tracking. International Journal of Computer and Systems Engineering, Vol:12, No:5,320-349
[3] Ardil, C. (2021). Architectural Acoustic Modeling for Predicting Reverberation Time in Room Acoustic Design Using Multiple Criteria Decision Making Analysis. International Journal of Architectural and Environmental Engineering, 15(9), 418 - 423.
[4] 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.
[5] 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.
[6] 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.
[7] Saaty, T. L. (1990). How to make a decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26. doi: 10.1016/0377-2217(90)90057-I
[8] Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. doi: 10.1504/IJSSCI.2008.017590
[9] Saaty, T.L. (1980). Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York.
[10] 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.
[11] 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.
[12] Ardil, C. (2018) Multidimensional Compromise Optimization for Development Ranking of the Gulf Cooperation Council Countries and Turkey. International Journal of Mathematical and Computational Sciences Vol:12, No:6, 131-138.
[13] Ardil, C. (2018) Multidimensional Compromise Programming Evaluation of Digital Commerce Websites. International Journal of Computer and Information Engineering Vol:12, No:7, 556-563.
[14] Ardil, C. (2018) Multicriteria Decision Analysis for Development Ranking of Balkan Countries. International Journal of Computer and Information Engineering Vol:12, No:12, 1118-1125.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] Ardil, C. (2020). Software Product Quality Evaluation Model with Multiple Criteria Decision Making Analysis. International Journal of Computer and Information Engineering, 14(12), 486 - 502.
[21] Roy, B. (1991). The outranking approach and the foundation of ELECTRE methods. Theory and Decision, 31(1), 49–73.
[22] 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.
[23] 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.
[24] Brans, J., Ph. Vincke. (1985). A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management Science, 31(6), 647-656.
[25] 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.
[26] 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.
[27] Ardil, C. (2020) Facility Location Selection using Preference Programming. International Journal of Industrial and Systems Engineering, 14(1), 1 - 12.
[28] Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
[29] 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.
[30] Choudhary, D. and Shankar, R. (2012. A STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: a case study from India”, Energy, Vol. 42 No. 1, pp. 510-521.
[31] Zavadskas, E.K., Mardani, A., Turskis, Z., Jusoh, A., Nor, K.M. (2016) Development of TOPSIS method to solve complicated decision-making problems: An overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15, 645-682.
[32] Ardil, C. (2019) Scholar Index for Research Performance Evaluation Using Multiple Criteria Decision Making Analysis. International Journal of Educational and Pedagogical Sciences, Vol:13, No:2, 93-105.
[33] 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.
[34] 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.
[35] Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. PhD Thesis, Faculty of Civil Engineering, Belgrade (in Serbian).
[36] Opricovic, S. (2007). A fuzzy compromise solution for multicriteria problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(3), 363–380.
[37] 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.
[38] Modarres, M., Sadi-Nezhad, S. (2005). Fuzzy simple additive weighting method by preference ratio, Intelligent Automation and Soft Computing, 235-244.
[39] Kaur, P., Kumar, S. (2013). An Intuitionistic Fuzzy Simple Additive Weighting Method for selection of vendor. ISOR Journal Business and Management, 78-81.
[40] Sagar, M.K., Jayaswal, P., Kushwah, K. (2013). Exploring Fuzzy SAW Method for Maintenance strategy selection problem of Material Handling Equipment, (2013), ISSN 22 77 – 4106.
[41] Wang,Y.J. (2015). A fuzzy multi-criteria decision making model based on additive weighting method and preference relation, Applied Soft Computing, 30,412-420.
[42] Roszkowska, E., Kacprzak, D. (2016). The fuzzy saw and fuzzy TOPSIS procedures based on ordered fuzzy numbers. Inf. Sci., 369, 564-584.
[43] Zhang, L., Xu, X., Tao, L. (2013) Some Similarity Measures for Triangular Fuzzy Number and Their Applications in Multiple Criteria Group Decision-Making, Journal of Applied Mathematics, vol. 2013, Article ID 538261, 1-7 pages, 2013.
[44] 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.
[45] 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.
[46] 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.
[47] 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.
[48] Ardil, C. (2022). Fighter Aircraft Selection Using Neutrosophic Multiple Criteria Decision Making Analysis. International Journal of Computer and Systems Engineering, 16(1), 5 - 9.
[49] 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.
[50] 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.
[51] Chen, S.J., Hwang, C.L. (1992). Fuzzy Multiple Factor Decision Making Methods and Applications, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, Berlin.
[52] Dyer,J.S., Fishburn,P.C., Steuer,R.E., Wallenius,J., Zionts,S. (1992). Multiple criteria decision making, Multiattribute utility theory: The next ten years, Management Sci. 38 (5), 645–654.
[53] Delgado,M., Verdegay,J.L., Vila,M.A. (1992). Linguistic decision making models, Int. J. Intelligent System 7, 479–492.
[54] Herrera, F., Herrera-Viedma,E., Verdegay, J.L.(1996). A model of consensus in group decision making under linguistic assessments, Fuzzy Sets and Systems 78,73–87.
[55] Hsu, H.M., Chen, C.T. (1997). Fuzzy credibility relation method for multiple criteria decision-making problems, Inform. Sci. 96,79–91.
[56] Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9.
[57] Chen, S. H., Wang, S. T., Chang, S. M. (2006). Some properties of Graded Mean Integration representation of L-R type fuzzy numbers. Tamsui Oxford Journal of Mathematical Sciences, 22(2), 185-208.
[58] Zadeh L.A., (1965). Fuzzy Sets. Information and Control, 8, 338-353.
[59] Bellman, R.E., Zadeh, L.A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141–164.
[60] Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning, Inform. Sci. 8, 199–249(I), 301–357(II).
[61] Atanassov K. (1986). Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, Vol. 20(1), 87-96.
[62] Atanassov K., Gargov G. (1989).Interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 31:343–349.
[63] Gau W.L., Buehrer D.J. (1993). Vague sets. IEEE Trans. Syst. Man Cybern. 23:610–613.
[64] Chen S.M. (1995). Measures of similarity between vague sets. Fuzzy Sets yst. 74:217–223.
[65] Bustince, H., Burillo, P.(1996). Vague sets are intuitionistic fuzzy sets.Fuzzy Sets and Systems 79, 403-405.
[66] Xu W., Ma J., Wang S., Hao G. (2010). Vague soft sets and their properties. Comput. Math. Appl.59:787–794.
[67] Wang C., Qu A. (2013). Entropy, similarity measure and distance measure of vague soft sets and their relations. Inf. Sci.244:92–106.
[68] Selvachandran, G., Garg, H., Quek, S. G. (2018). Vague Entropy Measure for Complex Vague Soft Sets. Entropy, 20(6), 403.
[69] Zhang,Q., Zhao, F.,Yang, J. (2019). The Uncertainty Analysis of Vague Sets
[70] Xu,G., Wang, Y., Leng, J. (2022). Improved PBFT Algorithm Based on Vague Sets. Sec. and Commun. Netw. 2022.
[71] Smarandache, F. (2019). Neutrosophic Set is a Generalization of Intuitionistic Fuzzy Set, Inconsistent Intuitionistic Fuzzy Set (Picture Fuzzy Set, Ternary Fuzzy Set), Pythagorean Fuzzy Set, Spherical Fuzzy Set, and q-Rung Orthopair Fuzzy Set, while Neutrosophication is a Generalization of Regret Theory, Grey System Theory, and Three-Ways Decision (revisited) . Journal of New Theory, (29), 1-31.
[72] Smarandache, F. (2018). Plithogenic Set, an Extension of Crisp, Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Sets - Revisited. Neutrosophic Sets and Systems: 21 / 2018 pp. 153-166.
[73] Smarandache, F. (2021). Plithogenic Probability & Statistics are generalizations of MultiVariate Probability & Statistics.Neutrosophic Sets and Systems, Vol. 45