12066 FACTA UNIVERSITATIS Series: Mechanical Engineering Vol. 21, No 3, Special Issue, 2023, pp. 387 - 403 https://doi.org/10.22190/FUME230824036R © 2023 by University of Niš, Serbia | Creative Commons License: CC BY-NC-ND Original scientific paper APPLICATION OF HYBRID DIBR-FUCOM-LMAW-BONFERRONI-GREY-EDAS MODEL IN MULTICRITERIA DECISION-MAKING Marko Radovanović1, Darko Božanić1, Duško Tešić1, Adis Puška2, Ibrahim M. Hezam3, Chiranjibe Jana4 1Military Academy, University of Defence in Belgrade, Belgrade, Serbia 2Department of Public Safety, Government of Brčko District of Bosnia and Herzegovina, Brčko, Bosnia and Herzegovina 3Statistics & Operations Research Department, College of Sciences, King Saud University, Riyadh, Saudi Arabia 4Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, India Abstract. The selection of unmanned aerial vehicles for different purposes is a frequent topic of research. This paper presents a hybrid model of an unmanned aerial vehicle (UAV) selection using the Defining Interrelationships Between Ranked criteria (DIBR), Full Consistency Method (FUCOM), Logarithm Methodology of Additive Weights (LMAW) and grey - Evaluation based on Distance from Average Solution (G-EDAS) methods. The above-mentioned model is tested and confirmed in a case study. First of all, in the paper are defined the criteria conditioning the selection, and then with the help of experts and by applying the DIBR, FUCOM and LMAW methods, the weight coefficients of the criteria are determined. The final values of the weight coefficients are obtained by aggregating the values of the criteria weights from all the three methods using the Bonferroni aggregator. Ranking and selection of the optimal UAV from twenty- three defined alternatives is carried out using the G-EDAS method. Sensitivity analysis confirmed a high degree of consistency of the solutions obtained using other MCDM methods, as well as changing the criteria weight coefficients. The proposed model has proved to be stable; its application is also possible in other areas and it is a reliable tool for decision-makers during the selection process. Key words: DIBR, FUCOM, LMAW, grey numbers, EDAS, unmanned aerial vehicle (UAV) Received: August 24, 2023 / Accepted October 08, 2023 Corresponding author: Darko Božanić Military Academy, University of Defence in Belgrade, Veljka Lukica Kurjaka 33, 11040, Belgrade, Serbia E-mail: darko.bozanic@va.mod.gov.rs 388 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA 1. INTRODUCTION Various UAVs are being used increasingly in various areas of human activity, both in civil and military sectors. Successful planning, organization and execution of modern military operations are based on information about the enemy, time and space, which can be collected using various systems, including UAVs. The global demand for UAVs is on the rise, as they are becoming increasingly crucial in enhancing the operational effectiveness of military units, positioning them as a vital component within combat systems [1]. Diverse possibilities lay the foundation for their utilization across various conditions and operational contexts. The possibilities of using UAV in combat operations are diverse: mine detection, electronic reconnaissance, electronic countermeasures, hyperspectral scanning, laser target marking, radar imaging, radar jamming, use as combat stations and anti-drone systems, management and leadership of units on the battlefield in the operation zone, as logistics stations, as transport support, as support for medical teams, etc. An UAV is characterized as an aerial vehicle equipped with an engine, which is either operated remotely by a controller or possesses a certain level of autonomous capability, the use of which is one-time or possiblly can be used multiple times and can carry different types of cargo. They differ in terms of purpose, construction characteristics and the source of energy used to propel them. The UAV are often called drones and and if these are used for combat then are called Unmanned Combat Air Vehicle (UCAV). Various possibilities of UAVs application make a significant contribution to defence and security, but these are also used in agriculture [2], building, transportation [3], commerce, transmission, research [4], medicine [5,6], science, architecture, archeology [7], visual recording and imaging [8,9], photogramemetry [10], geology and forestry [11], fire fighting [12], mining, oceanography and meteorology [13] ], environmental management, sports [14], mapping [15], etc. It is also possible to use them for recording, photography [16], monitoring [17], reconnaissance [18], detection [19], transport of various types of cargo [20], protection of important persons and objects, etc. The mentioned functions are significant and objective features for the implementation of unmanned aerial vehicles in various military units [21], because their utilization, there is a notable enhancement in the combat capabilities of units throughout the planning, arrangement, and execution of various military maneuvers. By selecting the optimal combat UAV and equipping army units, four very important capabilities would be significantly improved and these are the abilities to: command and lead, use the information space, efficiently deploy forces and be mobile in the area of operation. For the purpose of such selection, the multi-criteria DIBR-FUCOM-LMWA- Bonferonni-grey EDAS model is used. Up to now, a plethora of MCDM techniques have been formulated to assist in making specific decisions [22–25]. The very application of MCDM methods to the problem of selecting the optimal UAV and the general application of these methods related to UAVs have been analyzed by numerous authors in their research. Therefore, Ali Karimoddini [26] selects the UAV and evaluates its performances in order to support bridge inspection using the AHP method. By applying hybrid model Interval Type-2 Fuzzy AHP and TOPSIS, it is analyzed the selection of MALE (Medium-Altitude Long-Endurance) UAV [27]. Radovanović et al. [28] examine the selection of UAV’s serving to the requirements of tactical army units through the application of a hybrid fuzzy AHP-VIKOR model. Hamurcu and Eren [29] perform the selection of an UAV with MCDM model AHP- TOPSIS for Application of Hybrid DIBR-FUCOM-LMAW-Bonferroni-Grey-EDAS Model in ... 389 army needs. Using fuzzy ANP method, Liu and Chan [30] analyze the importance of service quality indicators for drone-based imaging and photography. Karaşan and Kaya [31] employ the TOPSIS method to identify the optimal technology for controlling unmanned aerial vehicles in terms of efficiency. The application of UAVs in research related to the military sphere is presented in certain publications. Milić et al. [18] investigate the utilization of UAVs in particular types of operations. Adamski [32] investigates the use of UAVs in modern conflicts. Petrovski and Radovanović [15] examine the integration of UAVs with command and information systems. Jović [33] scrutinizes the application of UAVs within counter-terrorism operations. Petrovski et al. [34] show the use of UAVs supported by mobile applications in crisis management. According to the performed analysis, it can be concluded that the topic of an UAV/UCAV selection has been discussed in the literature so far, but there is room to approach the selection/ranking issue in a different way, respectively, by applying new MCDM methods. The contribution of this paper is combined. The primary importance of this paper lies in the definition of criteria important for the selection of the optimal UCAV. Another, no less important contribution is the improvement of the selection methodology, that is, the formation of a decision-making model drawing upon various MCDM techniques. Apart from the introductory section, the paper comprises additional five sections. In the second section it is made a brief description of the model, respectively, the applied methods. Through the third section, the definition and calculation of the weight coefficients of the criteria and the ranking of alternative solutions are performed, that is, the application of the model is presented. The fourth section addresses sensitivity analysis, while in the fifth section is conducted comparative examination of the outcomes in relation to alternative MCDM approaches. At the end of the paper, the conclusion of this research is provided. 2. MODEL DESCRIPTION The complexity of the research issue required the application of a hybrid model of MCDM, which is formed of the methods DIBR, FUCOM and LMAW for defining weight coefficients of criteria, as well as G-EDAS method for selecting the most favorable combat unmanned aerial vehicle, based on defined criteria, which is presented in the Fig. 1. The first phase of the model includes the definition of criteria and the calculation of their weights using the DIBR, FUCOM and LMWA methods, and in the second phase, the aggregation of the weight coefficients of criteria is carried out using the Bonferroni aggregator. In the third phase, the alternatives are defined and the initial decision-making matrix, after which the most favorable solution is selected using the G-EDAS method. The fourth phase presents the sensitivity analysis, while in the fifth phase of the model a comparison of the results with other methods is performed. In the following sections it is provided a brief description of the methods used. 390 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA Fig. 1 DIBR-FUCOM-LMAW-Bonferonni-grey EDAS model 2.1. DIBR method The DIBR method facilitates enhanced comprehension for decision makers regarding criteria relations by focusing solely on the connections between adjacent criteria [35]. This notably streamlines the process of deriving weight coefficients, especially in scenarios involving a substantial number of criteria. In general, with some methods that have found wide application in practice such as the AHP, in cases with an extensive array of criteria, upholding result consistency becomes more challenging; thus, this method employs a tree structure encompassing both main criteria and sub-criteria [36]. This method is still relatively underused in a significant part of the literature. Tešić et al. [37] use this method with the fuzzy MARCOS method for the selection of a location for overcoming obstacles. In the paper [38] is shown the combination of Fuzzy DIBR and Fuzzy-Rough EDAS methods. Lukić [39] used DIBR - WASPAS model to rank trading companies. The steps of the DIBR method are presented on the Fig. 2. 2.2. FUCOM method The FUCOM method is one of the newer methods. It was applied for the first in the MCMD model in 2018 in the paper [40]. The FUCOM method (in basic or modifed version) is used in decision-making in combination with many other methods. Popović et al. [41] use gray FUCOM-SWOT model. Biswas et al. [42] use a fuzzy fermatean FUCOM-CODAS in the selection of a smartphone. Feizi et al. [43] present the FUCOM- MOORA and FUCOM MOOSRA model. Kahn et al. [44] present a novel fuzzy FUCOM- QFD approach. Pamučar et al. [45] developed a MCDM model - fuzzy FUCOM- neutrosophic fuzzy MARCOS. Stević et al. [46] present the FUCOM-EDAS model in the transport. The steps of the FUCOM method are presented on the Fig. 3. Application of Hybrid DIBR-FUCOM-LMAW-Bonferroni-Grey-EDAS Model in ... 391 Fig. 2 Steps of the DIBR method [35] Fig. 3 Steps of the FUCOM method [40] 2.3. LMAW method The LMAW method can be applied to determine weight coefficients of criteria and to select the optimal alternatives from a set of offered ones. It was developed in the paper by Pamučar et al. [47]. It is applied in many fields to solve various research issues. Until now, the LMAW method has been used in basic and modified form [48-51]. On Fig. 4 are presented the steps of the LMAW method related only to defining weight coefficients of criteria. 392 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA Fig. 4 Steps of the LMAW method related to calculation of weight coefficients of criteria [47] 2.4. Basics of the Grey Theory The theory of grey numbers serves as a potent approach for addressing challenges involving partially known information. Represented by ⊗x, a grey number denotes a value whose precise magnitude is not known, yet its belonging range is established. When upper �̅� and lower 𝑥 bounds are known, but specifics about the distribution for x' are uncertain, it's referred to as an interval grey number [52]. ⨂𝑥 = [𝑥, 𝑥] = [𝑥′ ∈ 𝑥|𝑥 ≤ 𝑥′ ≤ 𝑥] (1) The degree of greyness presents the distance between their limits 𝑥 − 𝑥. Depending of the degree of greyness interval number can become black number, white number or white (crisp) number. Further are presented the basic operations of interval grey numbers [53]: ⨂𝑥1 + ⨂𝑥2 = [𝑥1 + 𝑥2, 𝑥1 + 𝑥2] (2) ⨂𝑥1 − ⨂𝑥2 = [𝑥1 − 𝑥2, 𝑥1 − 𝑥2] (3) ⨂𝑥1 × ⨂𝑥2 = [𝑥1𝑥2, 𝑥1𝑥2] (4) ⨂𝑥1 ÷ ⨂𝑥2 = [ 𝑥1 𝑥2 , 𝑥1 𝑥2 ] (5) 𝑒⨂ 𝑥1 = 𝑒⨂[𝑥1, 𝑥1] = [𝑒𝑥1, 𝑒𝑥1] (6) To determine the crisp (whitened) value 𝑥(𝛿) for the interval grey number (⨂𝑥 = [𝑥, 𝑥]), the following expression is to be applied [48]: 𝑥(δ) = (1 − δ) 𝑥 + δ𝑥 (7) where δ0,1] and present the whitening coefficient. Application of Hybrid DIBR-FUCOM-LMAW-Bonferroni-Grey-EDAS Model in ... 393 2.5. Grey EDAS method The EDAS method was presented by Keshavarz Ghorabaee et al. [54]. The method has so far been applied in its basic form [55,56], as well as in fuzzy environment [57-62], grey environment [63,64] and rough environment [65]. The Fig. 5 shows the general steps of the G-EDAS method. Fig. 5 General steps of the grey EDAS method [64] 394 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA 3. DEFINING THE CRITERIA AND APPLICATION OF THE MCDM MODEL A large number of unmanned aerial vehicles with different characteristics and applications can be found on the market. The military requirements for optimal unmanned aerial vehicles for use in various operations are non-uniform in terms of their tactical, technical and economic characteristics. On the basis of the above, it is defined the goal of selecting the optimal solution (unmanned aerial vehicle) which would best meet the needs of the armed forces in terms of its tactical, technical and economic characteristics. The solution obtained by this research can be used in equipping army units with unmanned aerial vehicles. In the first phase of the application of the MCDM model are defined the criteria influencing the selection of the best UCAVs, for the needs of the army's tactical units. The establishment of criteria is rooted in the examination of the existing literature and the insights provided by subject matter experts (Table 1). Table 1 Criteria determining the selection of UCAVs Criterion Name of the Criterion Type of criteria Unit of measure C1 Flight autonomy Benefit hours (h) C2 Maximum flight range Benefit kilometers (km) C3 Weapon features of UCAV Benefit Linguistic C4 Reliability Benefit Linguistic C5 Maximum mass of the payload Benefit Linguistic C6 Proce of one UCAV Cost US dollars ($) C7 Maximum flight height Benefit meters (m) C8 Maximum speed Benefit km/h For the linguistic-type criteria it is used the scale presented in the Table 2. Table 2 The scale for attribute evaluations ⨂𝑨 [66] Scale ⨂𝐴 Very poor (VP) [0,1] Poor (P) [1,3] Medium poor (MP) [3,4] Fair (F) [4,5] Medium good (MG) [5,6] Good (G) [6,9] Very good (VG) [9,10] Based on the mentioned criteria, six experts defined the comparison values according to all the methods (DIBR, FUCOM, LMAW), after which the experts' viewpoints were consolidated using the Bonferroni aggregation method [67]: 𝐵𝑀𝑟,𝑠(𝑥1, 𝑥2 … , 𝑥𝑛) = ( 1 𝑛(𝑛−1) ∑ 𝑥𝑖 𝑟𝑥𝑗 𝑠𝑛 𝑖,𝑗=1 𝑖≠𝑗 ) 1 𝑟+𝑠 (18) Application of Hybrid DIBR-FUCOM-LMAW-Bonferroni-Grey-EDAS Model in ... 395 Once the criteria have been established, in the initial phase are calculated their weight coefficients using the DIBR approach. Implementing the DIBR method's procedures, weight coefficients are computed individually for each of the six experts (Table 3). In Table 3 are further presented the aggregated values of criteria weight coefficients. Table 3 Values of the weight coefficients of the criteria - DIBR method Criteria E1 E2 E3 E4 E5 E6 Aggregated values C1 0.199 0.215 0.229 0.221 0.232 0.273 0.228 C2 0.163 0.169 0.173 0.182 0.174 0.181 0.174 C3 0.151 0.150 0.141 0.155 0.143 0.131 0.145 C4 0.128 0.113 0.111 0.117 0.112 0.103 0.114 C5 0.097 0.096 0.091 0.092 0.096 0.084 0.093 C6 0.082 0.079 0.077 0.075 0.081 0.072 0.078 C7 0.067 0.067 0.066 0.061 0.064 0.059 0.064 C8 0.060 0.057 0.058 0.052 0.052 0.052 0.055 C9 0.053 0.054 0.054 0.045 0.046 0.045 0.049 Using the FUCOM method, the results of the weight coefficients of the criteria are obtained and presented in the Table 4. The table 4 also shows the aggregated weight coefficients. Table 4 Values of the weight coefficients of the criteria - FUCOM method Criteria E1 E2 E3 E4 E5 E6 Aggregated values C1 0.217 0.239 0.194 0.225 0.196 0.237 0.218 C2 0.168 0.159 0.151 0.150 0.168 0.140 0.156 C3 0.145 0.120 0.109 0.114 0.130 0.119 0.123 C4 0.108 0.109 0.123 0.108 0.117 0.103 0.111 C5 0.094 0.092 0.104 0.100 0.098 0.095 0.097 C6 0.080 0.080 0.091 0.090 0.087 0.085 0.086 C7 0.072 0.073 0.082 0.077 0.078 0.079 0.077 C8 0.062 0.068 0.078 0.073 0.067 0.074 0.070 C9 0.054 0.060 0.068 0.063 0.059 0.068 0.062 Using the LMAW method, the results of the weight coefficients of the criteria are obtained and presented in the Table 5. The table 5 also shows the aggregated weight coefficients. 396 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA Table 5 Values of weight coefficients of criteria - LMAW method Criteria E1 E2 E3 E4 E5 E6 Aggregated values C1 0.141 0.143 0.130 0.142 0.127 0.149 0.139 C2 0.134 0.137 0.136 0.128 0.133 0.142 0.135 C3 0.134 0.130 0.129 0.136 0.120 0.126 0.129 C4 0.127 0.129 0.123 0.120 0.127 0.135 0.127 C5 0.119 0.121 0.115 0.111 0.112 0.116 0.116 C6 0.109 0.111 0.115 0.111 0.093 0.126 0.111 C7 0.109 0.100 0.106 0.099 0.104 0.090 0.101 C8 0.085 0.086 0.106 0.086 0.104 0.071 0.090 C9 0.042 0.043 0.040 0.067 0.080 0.045 0.052 The final criteria weight coefficients result from the aggregation of the criteria weight coefficients derived through the DIBR, FUCOM, and LMAW approaches, as illustrated in the Table 6. Table 6 Final values of the weight coefficients of the criteria Criteria C1 C2 C3 C4 C5 C6 C7 C8 C9 Weight 0.194 0.155 0.133 0.118 0.103 0.092 0.080 0.071 0.054 Upon the establishing of the final weight coefficient values of the criteria, the process of selecting the optimal alternative from the set of potential ones is done using the G-EDAS technique. Six experts also participated in the evaluation of the alternatives. Aggregation of experts' opinions is carried out using the expression [66]: ⨂𝑥𝑗 = 1 𝐸 (⨂𝑥𝑗 1 + ⨂𝑥𝑗 2 + ⋯ + ⨂𝑥𝑗 𝐸) (19) where E presents the total number of experts. The initial phase in implementing this approach involves formulating the initial decision matrix, as presented in the Table 7. The values in the table present the aggregated values of the evaluations of all alternatives according to all criteria by each of the experts, obtained by applying the expression (19). Table 7 Initial decision-making matrix C1 C2 C3 ... C7 C8 C9 l1 u1 l2 u2 l3 u3 ... l7 u7 l8 u8 l9 u9 A1 27 40 200 300 5.50 7.50 ... 5500 7620 185 220 6.500 9.167 A2 30 42 300 500 6.83 8.83 ... 6000 8530 250 300 8.000 9.667 A3 20 28 1500 1852 4.00 5.00 ... 12000 15240 400 482 6.333 8.667 A4 40 46 900 1100 3.00 4.17 ... 6000 7620 180 217 4.000 5.000 … … … … … … … ... … … … … … … A20 52 60 750 1000 7.00 9.33 ... 7500 10000 250 280 3.333 4.333 A21 33 40 6000 7400 7.83 9.17 ... 10000 14000 350 407 4.500 5.500 A22 32 39 750 1000 6.17 8.17 ... 7500 9900 330 370 7.500 9.500 A23 40 46 20000 22800 4.50 5.50 ... 15000 18000 550 629 6.500 9.167 Application of Hybrid DIBR-FUCOM-LMAW-Bonferroni-Grey-EDAS Model in ... 397 After applying the steps of the G-EDAS method, the final ranking of the alternatives was obtained, table 8. Table 8 Rank of the alternatives using the G-EDAS method Alternatives Φi Ranking Alternatives Φi Ranking A1 0.346 15 A13 0.230 17 A2 0.403 11 A14 0.122 23 A3 0.516 7 A15 0.127 22 A4 0.379 12 A16 0.217 18 A5 0.590 3 A17 0.194 20 A6 0.308 16 A18 0.518 6 A7 0.567 5 A19 0.486 8 A8 0.581 4 A20 0.372 13 A9 0.361 14 A21 0.680 2 A10 0.164 21 A22 0.414 10 A11 0.207 19 A23 0.690 1 A12 0.477 9 4. SENSITIVITY ANALYSIS OF THE MODEL Sensitivity analysis typically encompasses the observation of shifts in alternative rankings when modifications are applied to the weight coefficients of the criteria. [68-70]. Regarding the changing of criteria weight coefficients, various methodologies exist. In this study, a distinct criterion is highlighted in each scenario. The research outlines and presents ten scenarios involving changes to the weight coefficients of the criteria, as detailed in Table 9. Table 9 Values of the weight coefficients of the criteria in relation to the scenario Criteria S0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 C1 0.194 0.111 0.304 0.087 0.087 0.087 0.087 0.087 0.087 0.087 0.087 C2 0.155 0.111 0.087 0.304 0.087 0.087 0.087 0.087 0.087 0.087 0.087 C3 0.133 0.111 0.087 0.087 0.304 0.087 0.087 0.087 0.087 0.087 0.087 C4 0.118 0.111 0.087 0.087 0.087 0.304 0.087 0.087 0.087 0.087 0.087 C5 0.103 0.111 0.087 0.087 0.087 0.087 0.304 0.087 0.087 0.087 0.087 C6 0.092 0.111 0.087 0.087 0.087 0.087 0.087 0.304 0.087 0.087 0.087 C7 0.08 0.111 0.087 0.087 0.087 0.087 0.087 0.087 0.304 0.087 0.087 C8 0.071 0.111 0.087 0.087 0.087 0.087 0.087 0.087 0.087 0.304 0.087 C9 0.054 0.112 0.087 0.087 0.087 0.087 0.087 0.087 0.087 0.087 0.304 The results obtained in relation to the defined scenarios are shown in the Fig. 6. 398 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA Fig. 6 Change in the ranks of alternatives depending on the change of the weight In the Fig. 7 is displayed the Spearman's rank correlation coefficient for the changes in criteria weight coefficients concerning the initial ones. This figure reveals that across all scenarios, the outcomes consistently exhibit a trend towards a positive correlation, aligning closely with the ideal. Notably, the most pronounced deviation arises in the scenarios S7 to S10, wherein the priority is predominantly assigned to the criteria initially possessing lower weight coefficients. This particular trend indicates a logical disarray within the system. Fig. 7 The values of the Spearman’s coefficient of the rank correlations. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 S-0 S-1 S-2 S-3 S-4 S-5 S-6 S-7 S-8 S-9 S-10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 S-0 S-1 S-2 S-3 S-4 S-5S-6 S-7 S-8 S-9 S-10 Application of Hybrid DIBR-FUCOM-LMAW-Bonferroni-Grey-EDAS Model in ... 399 5. COMPARATIVE ANALYSIS In order to compare the obtained results with the results of other methods, the following methods are used: gray COPRAS [71], MABAC [72], ARAS [73], COCOSO [74], WASPAS [75], MAIRCA [76], gray TOPSIS [77], gray MARCOS [78] and gray OCRA [79,80]. In the Fig. 8 are shown the ranks of alternatives depending on the method used to select the optimal solution. Fig. 8 Rank of alternatives depending on the MCDM model Based on the results shown in the Fig. 8, it can be clearly observed that the alternative A23 has a high degree of stability, because it took the first position in applying ten different methods of MCDM, respectively, it presents the optimal solution. The alternative A21, the second on the ranking list, occupies the same position in 9 different methods, while in only one method it took the third place, which can be considered extremely stable rating. 6. CONCLUSION The paper proposes a new hybrid MCDM model integrating the DIBR-FUCOM- LMAW methods, the results of which are aggregated using the Bonferroni aggregator with the grey EDAS method. The MCDM model is presented on the problem of selecting combat unmanned aerial vehicles that are in operational use by different armies of the world in order to equip tactical units of the Serbian Army. The DIBR, FUCOM and LMAW methods are used to determine the weight coefficients of the criteria, previously defined by experts and by analyzing available literature. By applying grey numbers integrated in the EDAS method, the best alternative is selected from a set of 23 different combat unmanned aerial vehicles. To validate the outcomes generated by the proposed model, a sensitivity analysis is conducted to assess the responsiveness of the output results to variations in 0 5 10 15 20 25 G-EDAS G-COPRAS MABAC ARAS COCOSO WASPAS MAIRCA G-TOPSIS G-MARCOS G-OCRA A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 400 M. RADOVANOVIĆ, D. BOŽANIĆ, D. TEŠIĆ, A. PUŠKA, I. M. HEZAM, C. JANA criteria weight coefficients. Additionally, a comparison is made between the results obtained by the proposed model and those provided by other methods. The results stemming from this analysis and comparison collectively affirm the stability of the established model. 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