Hostname: page-component-745bb68f8f-lrblm Total loading time: 0 Render date: 2025-01-23T12:24:47.772Z Has data issue: false hasContentIssue false
  • English
  • Français

Fighter optimal selection based on sequential multi-criteria decision-making with uncertainty measurement

Published online by Cambridge University Press:  22 January 2025

M. Suo ,
J. Xing Open the ORCID record for J. Xing [Opens in a new window] ,
K. Ma ,
D. Xiao Open the ORCID record for D. Xiao [Opens in a new window]  and
D. Song Open the ORCID record for D. Song [Opens in a new window]
M. Suo
Affiliation:
Institute of Reliability Engineering, Beihang University, Beijing, 100191, China National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, 100191, China School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
J. Xing
Affiliation:
Institute of Reliability Engineering, Beihang University, Beijing, 100191, China National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, 100191, China School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
K. Ma
Affiliation:
Institute of Reliability Engineering, Beihang University, Beijing, 100191, China National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, 100191, China School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
D. Xiao
Affiliation:
Institute of Reliability Engineering, Beihang University, Beijing, 100191, China National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, 100191, China School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
D. Song*
Affiliation:
Institute of Reliability Engineering, Beihang University, Beijing, 100191, China National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, 100191, China School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
*
Corresponding author: D. Song; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Rapid and comprehensive fighter optimisation is an important part of modern combat decision-making. However, due to the numerous influencing factors, it is difficult for decision-makers to consider comprehensively and specify the optimal decision, and it is highly subjective, which leads to different decision conclusions from person to person. Therefore, to solve the above deficiencies in fighter selection, this paper proposes a sequential decision-making framework that comprehensively considers the effectiveness, maintenance, support capability and health status of the fighter aircraft. Based on the multi-dimensional state, it provides comprehensive and credible auxiliary support for commanders. The sequential decision-making framework (called GRA-VIKOR-IFNs) uses the combination of equation and fuzzy multi-criteria decision-making (FMCDM) to evaluate the effectiveness, support capability and health in turn, to complete the step-by-step selection of fighter models, troops and sorties. The evaluation equation is for the effectiveness evaluation and a hybrid method using the extended grey correlation analysis (GRA) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on intuitionistic fuzzy numbers (IFNs) is for the support capability and health evaluation. The proposed strategy is in line with the logic and demand of actual combat and training decision-making and takes into account the influence of uncertain factors. Finally, a comparison with some classical methods is carried out, such as the full consistency method (FUCOM), the technique for order of preference by similarity to ideal solution (TOPSIS) and so on. The GRA-VIKOR-IFNs method is consistent with the results of other methods and the result sort resolution is 0.0619 and at least 40% higher than other methods, which can lead the commanders to a more reliable and clear decision.

Keywords

Fighter optimal selectionMulti-criteria decision-makingSequential decision makingEffectiveness evaluationIntuitionistic fuzzy numbers (IFNs)
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 23
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Mingxi, W. Intelligent Warfare: Prospects of Military Development in the Age of AI (1st ed.), 2022.Google Scholar
Özdemir, E. Warfare, strategies and tactics of, In Kurtz, L.R. (Ed.), Encyclopedia of Violence, Peace, & Conflict (3rd ed.), pp 275–284. Oxford: Academic Press.Google Scholar
Ardil, C. Military Fighter Aircraft Selection Using Multiplicative Multiple Criteria Decision Making Analysis Method, 2019.Google Scholar
Ma, S., Zhang, H. and Yang, G. Target threat level assessment based on cloud model under fuzzy and uncertain conditions in air combat simulation, Aerosp. Sci. Technol., 2017, 67, pp 49–53. https://doi.org/10.1016/j.ast.2017.03.033 CrossRefGoogle Scholar
Karamaşa, Ç., Karabasevic, D., Stanujkic, D., Kookhdan, A.R., Mishra, A.R. and Ertürk, M. An extended single-valued neutrosophic AHP and MULTIMOORA method to evaluate the optimal training aircraft for flight training organizations, Facta Univ. Ser. Mech. Eng., 2021, 19, (3), pp 555–578. https://doi.org/10.22190/FUME210521059K Google Scholar
Sánchez-Lozano, J.M., Correa-Rubio, J.C. and Fernández-Martínez, M. A double fuzzy multi-criteria analysis to evaluate international high-performance aircrafts for defense purposes, Eng. Appl. Artif. Intell., 2022, 115, p 105339. https://doi.org/10.1016/j.engappai.2022.105339 CrossRefGoogle Scholar
Lin, L., Luo, B. and Zhong, S. Development and application of maintenance decision-making support system for aircraft fleet, Adv. Eng. Softw., 2017, 114, pp 192–207. https://doi.org/10.1016/j.advengsoft.2017.07.001 CrossRefGoogle Scholar
Pedersen, T.I. and Vatn, J. Optimizing a condition-based maintenance policy by taking the preferences of a risk-averse decision maker into account. Reliab. Eng. Syst. Saf., 2022, 228, p 108775. https://doi.org/10.1016/j.ress.2022.108775 CrossRefGoogle Scholar
Sahoo, S.K. and Goswami, S.S. A comprehensive review of Multiple Criteria Decision-Making (MCDM) methods: Advancements, applications, and future directions, Decis. Mak. Adv., 2023, 1, (1), pp 25–48. https://doi.org/10.31181/dma1120237 CrossRefGoogle Scholar
Nezhad, M.Z., Nazarian-Jashnabadi, J., Rezazadeh, J., Mehraeen, M. and Bagheri, R. Assessing dimensions influencing IoT implementation readiness in industries: A fuzzy DEMATEL and Fuzzy AHP analysis, J. Soft Comput. Decis. Analy., 2023, 1, (1), pp 102–123. https://doi.org/10.31181/jscda11202312 CrossRefGoogle Scholar
Jiao, J.Y. and IOP. Research on equipment maintenance support based on fuzzy multiple attribute decision making. In Paper presented at the 3rd International Conference on Advances in Energy, Environment and Chemical Engineering.Google Scholar
Wei, S.D., Xing, G.P., Sun, D.X., Gao, K. and Liu, Y.W. Research on SPA-based approaches and application of the evaluation for maintenance quality of military aircraft. In Paper presented at the 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 2011.CrossRefGoogle Scholar
Ju-Long, D. Control problems of grey systems. Systems Contr. Lett., 1982, 1, (5), pp 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X CrossRefGoogle Scholar
Opricovic, S. Multicriteria Optimization of Civil Engineering Systems, 1998.Google Scholar
Atanassov, K.T. Intuitionistic fuzzy sets, Fuzzy Sets Syst., 1986, 20, (1), pp 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3 CrossRefGoogle Scholar
Hwang, C.-L. and Yoon, K. Multiple attribute decision making: Methods and applications - A state-of-the-art survey. In Paper Presented at the Lecture Notes in Economics and Mathematical Systems, 1981.CrossRefGoogle Scholar
Zhang, S.-F., Liu, S.-Y. and Zhai, R.-H. An extended GRA method for MCDM with interval-valued triangular fuzzy assessments and unknown weights. ScienceDirect 2011, 61, (4), pp 1336–1341.Google Scholar
Zadeh, L.A. Fuzzy sets. Inform. Contr., 1965, 8, (3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X CrossRefGoogle Scholar
Zhu Baoliu, Z.R. and Xiaofei, X. Fighter Plane Effectiveness Assessment (Second Edition). Beijing: Aviation Industry Press, 2006 (in Chinese).Google Scholar
Ge, Y., Xiao, M., Yang, Z. and Hou, X. Research on complex weapon system maintenance decision-making under uncertainty, Proc. Comput. Sci., 2018, 131, pp 887–894. https://doi.org/10.1016/j.procs.2018.04.296 CrossRefGoogle Scholar
Cheng, C.-H. Evaluating weapon systems using ranking fuzzy numbers, Fuzzy Sets Syst., 1999, 107, (1), pp 25–35. https://doi.org/10.1016/S0165-0114(97)00348-5 CrossRefGoogle Scholar
Rausand, M. Reliability centered maintenance, Reliabil. Eng. Syst. Safety, 1998, 60, (2), pp 121–132. https://doi.org/10.1016/S0951-8320(98)83005-6 CrossRefGoogle Scholar
Asuquo, M.P., Wang, J., Zhang, L. and Phylip-Jones, G. Application of a multiple attribute group decision making (MAGDM) model for selecting appropriate maintenance strategy for marine and offshore machinery operations, Ocean Eng., 2019, 179, pp 246–260. https://doi.org/10.1016/j.oceaneng.2019.02.065 CrossRefGoogle Scholar
Vogl, G.W., Weiss, B.A. and Helu, M. A review of diagnostic and prognostic capabilities and best practices for manufacturing, J. Intell. Manuf., 2019, 30, (1), pp 79–95. https://doi.org/10.1007/s10845-016-1228-8 CrossRefGoogle ScholarPubMed
Ranasinghe, K., Sabatini, R., Gardi, A., Bijjahalli, S., Kapoor, R., Fahey, T. and Thangavel, K. Advances in integrated system health management for mission-essential and safety-critical aerospace applications, Progr. Aerosp. Sci., 2022, 128, p 100758. https://doi.org/10.1016/j.paerosci.2021.100758 CrossRefGoogle Scholar
Jing, L., He, S., Ma, J., Xie, J., Zhou, H., Gao, F. and Jiang, S. Conceptual design evaluation considering the ambiguity semantic variables fusion with conflict beliefs: An integrated Dempster-Shafer evidence theory and intuitionistic fuzzy –VIKOR. Adv. Eng. Inform., 2021, 50, p 101426. https://doi.org/10.1016/j.aei.2021.101426 CrossRefGoogle Scholar
Dong Yanfei, H.T. Modeling method of fighter integrated combat effectiveness evaluation, Fire Contr. Comm. Contr., 2012, 37, pp 9–11.Google Scholar
Dong, Y., Wang, L. and Zhang, H. A comprehensive index model for air combat effectiveness evaluation of fighter aircraft, Acta Aeronaut. Astronaut. Sin., 2006, 2006, pp 1084–1087.Google Scholar
Huang Guoqing, W.G. and Zang, Q. Research on air combat effectiveness evaluation system of fighter with adjustable parameters, Electron. Optics Contr., 2013, 20, pp 33–36.Google Scholar
Wang, P., Meng, P., Zhai, J.-Y. and Zhu, Z.-Q. A hybrid method using experiment design and grey relational analysis for multiple criteria decision making problems. Knowledge-Based Systems, 2013, 53, 100–107. https://doi.org/10.1016/j.knosys.2013.08.025 CrossRefGoogle Scholar
Shen Maoxing, X.X. and Zhang, X. Selection of resolution coefficient in grey relational analysis, J. Air Force Eng. Univ. (Nat. Sci. Ed.), 2003, 01, 68–70 (in Chinese).Google Scholar
Pamučar, D., Stević, Ž. and Sremac, S. A new model for determiningweight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM), Symmetry, 2018, 10, (9). https://doi.org/10.3390/sym10090393 CrossRefGoogle Scholar
Ranjan, M.J., Kumar, B.P., Bhavani, T.D., Padmavathi, A.V. and Bakka, V. Probabilistic linguistic q-rung orthopair fuzzy Archimedean aggregation operators for group decision-making, Decis. Mak. Appl. Manag. Eng., 2023, 6, (2), pp 639–667. https://doi.org/10.31181/dmame622023527 CrossRefGoogle Scholar
Žižovic, M. and Pamucar, D. New model for determining criteria weights: Level Based Weight Assessment (LBWA) model, Decis. Mak. Appl. Manag. Eng., 2019, 2, (2), pp 126–137. https://doi.org/10.31181/dmame1902102z CrossRefGoogle Scholar
Ataei, Y., Mahmoudi, A., Feylizadeh, M.R. and Li, D.-F. Ordinal Priority Approach (OPA) in multiple attribute decision-making, Appl. Soft Comput., 2020, 86, p 105893. https://doi.org/10.1016/j.asoc.2019.105893 CrossRefGoogle Scholar
Li, Z., Luo, Z., Wang, Y., Fan, G., and Zhang, J. Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method. Renewable Energy, 2022, 184, pp 564–576. https://doi.org/10.1016/j.renene.2021.11.112.CrossRefGoogle Scholar
Mingxing, L. Research on Equipment Optimization Method in Marine Environment Based on Multi-attribute Decision Making (Master), National University of Defense Technology, 2009 (in Chinese).Google Scholar
Boran, F.E., Genç, S., Kurt, M. and Akay, D. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Exp. Syst. Appl., 2009, 36, (8), pp 11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039 CrossRefGoogle Scholar
Pamučar, D. and Ćirović, G. The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC), Exp. Syst. Appl., 2015, 42, (6), pp 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057 CrossRefGoogle Scholar
Wang, J., Wei, G., Wei, C. and Wei, Y. MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment, Def. Technol., 2020, 16, (1), pp 208–216. https://doi.org/10.1016/j.dt.2019.06.019 CrossRefGoogle Scholar
Pamučar, D., Lukovac, V., Božanić, D. and Komazec, N. Multi-criteria FUCOM-MAIRCA Model for the Evaluation of Level Crossings: Case Study in the Republic of Serbia, 2019.Google Scholar
Pamucar, D.S., Tarle, S.P. and Parezanovic, T. New hybrid multi-criteria decision-making DEMATEL-MAIRCA model: sustainable selection of a location for the development of multimodal logistics centre, Econom. Res.-Ekonomska Istraživanja, 2018, 31(1), pp 1641–1665. https://doi.org10.1080/1331677X.2018.1506706 CrossRefGoogle Scholar