Fighter Aircraft Selection Using Fuzzy Preference Optimization Programming (POP)

Authors: C. Ardil

Abstract:

The Turkish Air Force needs to acquire a sixth- generation fighter aircraft in order to maintain its air superiority and dominance against its rivals under the risks posed by global geopolitical opportunities and threats. Accordingly, five evaluation criteria were determined to evaluate the sixth-generation fighter aircraft alternatives and to select the best one. Systematically, a new fuzzy preference optimization programming (POP) method is proposed to select the best sixth generation fighter aircraft in an uncertain environment. The POP technique considers both quantitative and qualitative evaluation criteria. To demonstrate the applicability and effectiveness of the proposed approach, it is applied to a multiple criteria decision-making problem to evaluate and select sixth-generation fighter aircraft. The results of the fuzzy POP method are compared with the results of the fuzzy TOPSIS approach to validate it. According to the comparative analysis, fuzzy POP and fuzzy TOPSIS methods get the same results. This demonstrates the applicability of the fuzzy POP technique to address the sixth-generation fighter selection problem.

Keywords: Fighter aircraft selection, sixth-generation fighter aircraft, fuzzy decision process, multiple criteria decision making, preference optimization programming, POP, TOPSIS, Kizilelma, MIUS, fuzzy set theory

References:

[1] 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.
[2] 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.
[3] 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.
[4] Ardil, C. (2022). Fighter Aircraft Selection Using Neutrosophic Multiple Criteria Decision Making Analysis. International Journal of Computer and Systems Engineering, 16(1), 5 - 9.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[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. (2021). Neutrosophic Multiple Criteria Decision Making Analysis Method for Selecting Stealth Fighter Aircraft. International Journal of Aerospace and Mechanical Engineering, 15(10), 459 - 463.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] Sánchez-Lozano, J.M., Correa-Rubio, J., Fernández-Martínez, M. (2022). A double fuzzy multi-criteria analysis to evaluate international high-performance aircrafts for defense purposes. Engineering Applications of Artificial Intelligence,Volume 115, October 2022, 105339.
[22] Maêda, S.M., Costa, I.P., Castro Junior, M.A., Fávero, L., Costa, A.P., Corriça, J.V., Gomes, C.F., & Santos, M.D. (2021). Multi-criteria analysis applied to aircraft selection by Brazilian Navy. Production.
[23] Sánchez-Lozano, J.M., Rodríguez, O.N. (2020). Application of Fuzzy Reference Ideal Method (FRIM) to the military advanced training aircraft selection. Appl. Soft Comput., 88, 106061.
[24] Kiracı, K., Akan, E. (2020). Aircraft selection by applying AHP and TOPSIS in interval type-2 fuzzy sets. Journal of Air Transport Management, 89, 101924 - 101924.
[25] Sánchez-Lozano, J.M., Serna, J., Dolón-Payán, A. (2015). Evaluating military training aircrafts through the combination of multi-criteria decision making processes with fuzzy logic. A case study in the Spanish Air Force Academy. Aerospace Science and Technology, 42, 58-65.
[26] 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
[27] 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
[28] Saaty, T.L. (1980). Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York.
[29] Roy, B. (1991). The outranking approach and the foundation of ELECTRE methods. Theory and Decision, 31(1), 49–73.
[30] 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.
[31] 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.
[32] Brans, J., Ph. Vincke. (1985). A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management Science, 31(6), 647-656.
[33] 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.
[34] 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.
[35] Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
[36] 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.
[37] 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.
[38] 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.
[39] Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. PhD Thesis, Faculty of Civil Engineering, Belgrade (in Serbian).
[40] Opricovic, S. (2007). A fuzzy compromise solution for multicriteria problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(3), 363–380.
[41] 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.
[42] Buckley,J.J. (1985). Fuzzy hierarchical analysis, Fuzzy Sets and Systems, 17, 233–247.
[43] Hsu, H.M., Chen, C.T. (1997). Fuzzy credibility relation method for multiple criteria decision-making problems, Inform. Sci. 96,79–91.
[44] Hsu, H.M., Chen, C.T. (1996). Aggregation of fuzzy opinions under group decision making, Fuzzy Sets and Systems, 79, 279–285.
[45] Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9.
[46] Bellman, R.E., Zadeh, L.A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141–164.
[47] Zadeh L.A., (1965). Fuzzy Sets. Information and Control, 8, 338-353.
[48] 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).
[49] Delgado, M., Verdegay, J.L., Vila, M.A. (1992). Linguistic decision making models, Int. J. Intelligent System 7, 479–492.
[50] 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.
[51] Kizilelma, https://baykartech.com/ accessed on 21.10. 2022.