Abstract
Purpose
The present paper assesses the decision problem of selecting Unmanned Aerial Vehicle Systems (SARP) by the hybrid MPSI-SPOTIS approach for deployment in border control and transborder illicit combat.
Design/methodology/approach
By the hybrid MCDA MPSI-SPOTIS approach, and from the database available in Gettinger (2019), models were filtered by Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload, fitting within the classification of Categories EB 0 and 2. Category EB 1 was not considered in this study due to the limited number of models in the data source.
Findings
The use of the Multi-Criteria Decision Analysis (MCDA) tool MPSI-SPOTIS allowed the determination of weights by stochastic criteria, applied in a ranking method resistant to reverse ordering. The application of the method identified the Raybird-3 (Cat EB 0) and Searcher (Mk3) (Cat EB 2) models as the best alternatives. From a proposed clustering, other selection possibilities with close performance in the evaluation were presented. The cost criterion was not taken into consideration due to the absence of information in the data source employed. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.
Research limitations/implications
The cost criterion was not taken into consideration due to the absence of information in the data source used. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.
Originality/value
This paper aims to propose a technology selection method applied to complex defense acquisitions when multiple factors influence the decision makers and it is hard to obtain a major optimum solution in multitask and multi-mission platform.
Keywords
Citation
Torres, P.S., Gomes, C.F.S. and Santos, M.d. (2024), "Selection of unmanned aerial vehicle systems for border monitoring using the MPSI-SPOTIS method", Journal of Defense Analytics and Logistics, Vol. 8 No. 1, pp. 80-104. https://doi.org/10.1108/JDAL-12-2023-0016
Publisher
:Emerald Publishing Limited
Copyright © 2024, Pablo Santos Torres, Carlos Francisco Simões Gomes and Marcos dos Santos
License
Published in the Journal of Defense Analytics and Logistics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
1.1 Practical and methodological aims of the study
This paper aims to explore the use of the Multi-Criteria Decision Analysis (MCDA) method MPSI-SPOTIS for the selection of UAVs (in Brazilian Portuguese – Sistemas de Aeronaves Remotamente Pilotadas - SARP) intended for border monitoring, considering the various demands associated with their use. This study is based on the framework proposed by the Center for the Study of the Drone – Bard College (Gettinger, 2019), which encompasses technical data on SARPs from various countries.
To achieve this goal, this paper approaches the research problem in an attempt to identify a proper MCDA method to evaluate SARPs in the context of board control in Brazil, satisfying the following objectives: provide a description of the problem context; identify MCDA methods applied to similar problems; describe the MPSI-SPOTIS method; and rank the SARPs by the application of MPSI-SPOTIS.
1.2 Motivation for conducting research
The United Nations (UN), aiming to drive global development, established the Sustainable Development Goals (SDGs), of which Brazil is a signatory (UN, 2023). This set of goals encompasses 17 development axes, with SDG 16 - Peace, Justice, and Strong Institutions - including the combat against transborder crimes (UN Brazil, 2015).
In this context, the Brazilian National Defense Policy, the Brazilian National Defense Strategy (Brazil, 2020a), and Complementary Law No. 97 and its amendments (Brazil, 1999) assign to the Armed Forces the responsibility of defending the national territory, ensuring sovereignty, maintaining law and order, as well as operating on the border. These documents established National Defense Objectives and oriented the participation of the Armed Forces in the fight against transborder crimes.
The Brazilian Army instituted the Strategic Program of the Army Integrated Border Monitoring System (SISFRON), part of the Integrated Border Protection Program. Its purpose is to promote “the integration and coordination of public security, intelligence, customs control, and the Armed Forces' actions with the actions of states and municipalities located in the border strip and neighboring countries” (Brazil, 2017).
SISFRON is a system composed of Systems and Materials for Military Use (SMEM), which aim to monitor, sense, and provide decision support in the Brazilian border strip. The use of technological solutions allows “enhancing the current Operational Capabilities of troops already deployed in this area of the national territory, whether in fulfilling their constitutional mission or acting in subsidiary assignments through cooperation with different agencies” (Brazil, 2017).
Among the essential components of SISFRON, the use of Unmanned Aerial Vehicle Systems (SARP) stands out. SARPs must have features such as the ability to communicate with other systems, support sensing and monitoring activities, and potentially transport cargo (Brazil, 2020b).
The Unmanned Aerial Vehicle Systems project (Brazil, 2020c) aims to establish the life cycle management of SARPs within the scope of the Brazilian Army. This document regulates the conditions for acquisition and logistics for the maintenance of SMEMs of Categories EB (Cat EB) 0 to 2 until the year 2035, within the context of the Army Strategic Program for Full Operational Capability Acquisition (Prg EE OCOP) (Brazil, 2020c, 2023).
Among the challenges outlined in the project initiation directive (Brazil, 2020c), the following stands out: the possibilities and the impacts of financing systems and materials obtained for the project, considering the life cycle of SMEM; obtaining SMEM, seeking to prioritize the national Defense Industrial Base (BID); reduction of technological gaps and external dependence on SMEM to be obtained; constant need for system replacement due to obsolescence, discontinuation, or wear and tear from use; and continuous study of the evolution of doctrine and new materials that can be adopted.
The main risks identified regarding the material include (Brazil, 2020c): acquisition of SMEM whose production line is closed or obsolete, leading to future difficulties in obtaining supplies; lack of capacity of Science, Technology, and Innovation (STI) institutions and/or national companies to develop or produce the material properly, making deliveries dependent on foreign technologies; discontinuity and/or insufficient budgetary resources for investment and financing; interruption of the supply of SARPs materials or loss of qualification of suppliers; and costs arising from frequent replacements of systems, especially in categories 0 and 1, due to wear and rapid obsolescence.
The Brazilian Land Operations Command (COTER), an organ of the Brazilian Army, established the guidelines for the use and responsibilities in SARPs (Brazil, 2023). In this document, an Unmanned Aerial Vehicle (UAV) is defined as “an airborne vehicle in which the operator is not on board (unmanned), being controlled remotely from a pilot station, for the execution of a specific activity or task” (Brazil, 2023).
1.3 The contributions of the study
This paper offers a MCDA approach to the assessment of technological solutions, particularly SARP systems, through the application of MPSI-SPOTIS methods. The MPSI weight definition method allows a suitable approach for the context of research based on the alternatives’ performance in each criterion, and the SPOTIS ranking method has the feature of reverse ranking resistance. Therefore, a novel viewpoint to tackle the complexity of military systems’ selection is proposed that considers the possibility of allowing the decision-maker to insert or withdraw alternatives without modifying the previous ranking.
1.4 Organization of the paper
This paper is divided into the following sections: Introduction (Section 1) provides initial insights of the problem context; Problem Description (Section 2) depicts essential aspects of SARPs systems; Literature Analysis (Section 3) presents a literature overview of MCDM methods applied to military technology solutions assessment; Methodology (Section 4) describes the MPSI-SPOTIS method approach; Solution of the Problem (Section 5) shows the application of MPSI-SPOTIS in SARP ranking, compares with others MCDA approaches and offers a sensitivity test; Results (Section 6) discusses the major insights of the obtained ranking; and Conclusion (Section 7) debates the research goals, insights, contributions, limitations and future researches.
2. Problem description
An Unmanned Aerial Vehicle System is a “set of means necessary for the accomplishment of a specific task using UAVs” (Brazil, 2023), composed of the parts presented in Figure 1.
Unmanned Aerial Vehicles (UAVs), or aerial platforms, include the propulsion system, electrical system, and embedded navigation and control system. The payload consists of sensors and equipment onboard for the fulfillment of the assigned mission, which may encompass electro-optical sensors, infrared sensors, radars, pointers, and laser systems, and Electronic Warfare (EW) systems (Brazil, 2023).
The Ground Control Station (GCS) serves as the interface between the operator, UAV, and payload. Depending on the category of the UAV, it can be portable or installed in vehicles or shelters. The GCS typically comprises the command for the UAV and the command for the payload. The Data Transmission Terminal (DTT) consists of equipment linking the aircraft to the GCS, combining both telemetry and coordination with air control (Brazil, 2023).
The Data Link Terminal (DLT) is composed of equipment that enables data flow between the GCS and the command-and-control center of the military unit of interest. Finally, the support infrastructure brings together resources and means to sustain logistical and maintenance aspects of the operation (Brazil, 2023).
To classify UAVs into groups, COTER proposed classes based on payload capacity called Categories EB (Cat EB), as outlined in Table 1.
The Brazilian Army’s need to acquire the capability to operate UAV-type aerial vehicles and the complexity associated with the selection of systems based on various criteria constitute a complex problem, suitable for the use of Multi-Criteria Decision Analysis (MCDA) methods. In this context, the MPSI-SPOTIS method emerges as a decision support tool applicable to a project of relevance to Brazil.
The center of the decision problem lies in the complexity of selecting from many alternatives of aerial platforms capable of fulfilling various missions they will be subjected to. The specific nature of a given task implies variations in the selection of payload, takeoff characteristics (MTOW), and the required flight time and distance. Figure 2 provides a rich picture of the inherent complexity of this decision-making process.
3. Literature analysis
In a broad approach, the application of MCDM methods in defense sciences has been the subject of many studies. Among the most recent, it is noteworthy the trending topics of evaluation of life cycle management (Girardi et al., 2022); selection of weapon systems (Radovanović et al., 2023, 2024), selection of 4 × 4 vehicles (Gavião et al., 2024), fighter aircraft (Rajukar et al., 2023); combat helicopters (Kurnaz et al., 2023); optimization of anti-aircraft missile system (Akram et al., 2024), anti-UAV systems selection (Moreira et al., 2023); defense supplier selection (Desticioglu Tasdemir and Asilogullari Ayan, 2024; Güneri and Deveci, 2023; Rasmussen et al., 2023); cybersecurity systems (Al-Hchaimi et al., 2023; Belal and Sundaram, 2023); strategy selection (Tešić et al., 2023) and technology evaluation (Girardi and Santos, 2023).
The main MCDA methods found were: AHP (Gavião et al., 2024), AHP-VIKOR (Radovanović et al., 2023), AHP-MOORA-VIKOR (Rajukar et al., 2023), AHP-TOPSIS-SECA (Rasmussen et al., 2023), AHP-TOPSIS-2N (Girardi and Santos, 2023), AHP-FTOPSIS (Desticioglu Tasdemir and Asilogullari Ayan, 2024), AHP-EDAS (Güneri and Deveci, 2023), LMAW-G-EDAS (Radovanović et al., 2024), ELECTRE III (Akram et al., 2024), F-FDOSM-CRITIC (Al-Hchaimi et al., 2023), MEREC-VIKOR (Belal and Sundaram, 2023), Grey SWARA (Kurwaz et al., 2023), DIBR-DOMBI-Fuzzy MAIRA (Tešić et al., 2023), and SAPEVO-H2 (Moreira et al., 2023).
Particularly, when analyzing the literature regarding SARP selection, the search descriptors (“unmanned aerial vehicles” OR “uav” OR “drone”) AND (“selection” OR “evaluation” OR “prioritization”) AND (“multicriteria” OR “multi-criteria”) were utilized in the Scopus database, retrieving 91 documents. The inclusion and exclusion criteria applied were: the time period between the years from 2018 to 2023; only papers, conference papers and journals as the source; English language; and a filter based on the title and abstract of documents related to the topic.
Figure 3 summarizes the literature search and filtering process, highlighting the stages of identification and selection of documents.
The bibliometric analysis conducted using the Biblioshiny software (Aria and Cuccurullo, 2017) provided general data from the field, as shown in Figure 4. Noteworthy is the growth trend in the subject’s interest (annual growth rate of 14.87%) and the significant international collaboration (35% co-authorship), indicating the global interest and relevance of the selection of SARPs.
Figure 5 describe the temporal evolution of the number of publications per year, with the trend line indicating the growth in the number of documents. The year 2021 exhibited the highest number of publications.
Table 2 presents the top 10 publication sources found, ordered by the number of documents and their H-index. Procedia Computer Science stood out with two publications. Overall, no clear prevalence of one source over another was demonstrated.
Figure 6 displays the most important authors, and Figure 7 shows the most relevant countries. Authors dos Santos, Gomes, and Moreira emerged as prominent figures, combined with Brazil’s leadership in scientific production in the field, indicating the formation of a significant research group.
Table 3 presents the top 15 publications in the field, ordered by Total Citations (TC). Notably, Moreira et al. (2020) and Moreira et al. (2021) appears twice, which, when combined with Figures 6 and 7, underscores the importance of Brazilian institutions in the field of MCDA.
Analyzing the history of the seminal paper on the SPOTIS method (Dezert et al., 2020) using the LITMAPS tool (n.d.), there is a substantial volume of related papers, indicating the consolidation of the MCDA method (Figure 8).
The MCDA methods used in the relevant publications (Table 3) include: AHP (Canetta et al., 2018), AHP-ANP-DEMATEL (Zhang et al., 2023), AHP-TOPSIS (Hamurcu and Eren, 2020; Dukić et al., 2022), TOPSIS (Karaşan and Kaya, 2020), Fuzzy TOPSIS (Topaloglu et al., 2018; Nur et al., 2020), DEMATEL (Raj and Sah, 2019), WASPAS (Simić et al., 2021), PROMETHEE-SAPEVO-M1 (Moreira et al., 2020, 2021), Spherical Fuzzy MARCOS (Kovač et al., 2021), CoCoSo (Pamucar et al., 2023), and SAGA (Mohamadi et al., 2021).
As for the types of problems addressed, 5 focused on UAV for military applications (Dukić et al., 2022; Hamurcu and Eren, 2020; Karaşan and Kaya, 2020; Moreira et al., 2020, 2021), 4 addressed drone product delivery (Kovač et al., 2021; Nur et al., 2020; Raj and Sah, 2019; Simić et al., 2021), 2 dealt with transportation systems and control (Mohamadi et al., 2021; Pamucar et al., 2023), and 1 focused on value chain analysis (Canetta et al., 2018), and environmental solutions (Topaloglu et al., 2018).
4. Methodology
This paper proposes a case study with a hybrid MCDA approach, combining the Modified Preference Selection Index (MPSI) (Gligorić et al., 2022) and the Stable Preference Ordering Towards Ideal Solution (SPOTIS) (Dezert et al., 2020). The choice of the MPSI method aims to obtain weights, eliminating the need to define the relative importance between criteria since it is calculated using stochastic concepts without the expression of criteria preferences by decision-makers. SPOTIS stands out for being a method designed to avoid reverse ordering, allowing the inclusion or exclusion of alternatives without altering the previous result.
The MPSI-SPOTIS hybrid approach explores the calculation of criterion weights through statistical measures of preference values from the MPSI method (Gligorić et al., 2022) and ranks the results obtained by SPOTIS. This approach allows flexibility to change the decision environment without causing reverse ordering due to changes in expressed preferences (Dezert et al., 2020). The constituent steps of this process are schematically presented in Figure 9.
4.1 MPSI method steps
The MPSI method proposed by Gligorić et al. (2022) comprises five steps. In Step 1, the definitions of objectives, criteria, alternatives, and preference values take place. Considering the set of alternatives
Step 2 involves normalizing the data by transforming values
Steps 3 and 4 involve obtaining the values of the preferences
4.2 SPOTIS method steps
The SPOTIS method, proposed by Dezert et al. (2020), presents five steps starting from the decision matrix
In step 1, the definition of criterion limits takes place, which is a subjective stage. Decision-makers must establish value intervals that meet the problem’s solution. For each criterion
In step 2, the Ideal Solution Points are defined, where the extreme value of each interval is selected based on the criteria’s objective. For monotonically benefit criteria, the highest value is selected, and for monotonically cost criteria, the lowest value is selected. Thus, each Ideal Solution Point
From
At last, the ranking is obtained by ascending order of each
5. Solution of the problem
Gettinger (2019) provides technical data for 168 SARPs currently in use or under development. As the scope of the SARP project in the Brazilian Army covers only Categories 0 to 2 (Table 1), this is the only alternative selection criteria in the database provided by Gettinger (2019) in this paper. The evaluated criteria chosen in this paper are Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload. These criteria were selected since they were the parameters available for most UAVs in the database.
The Endurance criterion, measured in hours, is related to the work hours that an UAV can last in operation, where the more it endures, the better (monotonic benefit). The Range criterion, as a monotonic benefit and measured in kilometers, describes the maximum distance that an UAV can travel while in operation. The MTOW criterion, a monotonic benefit measured in kilograms, expresses the maximum weight that an UAV can have to take off from an airbase. The Payload criterion, also a monotonic benefit measured in kilograms, represents the maximum cargo weight that an UAV can carry as additional equipment, such as sensors.
After filtering the UAVs in the database (alternatives) using the aforementioned criteria, 18 models from Category EB 0, 2 models from Category EB 1, and 13 models in Category EB 2 were selected, as shown in Table 4. Since only 2 models were found in Category EB 1, these models were not evaluated in this study.
From the data in Table 4, the decision matrix
Based on the normalized decision matrix (Table 5), the process of obtaining the weights is started by the calculation of the vectors
Returning to the decision matrix
Using the values of
With the data from Table 8, the values of
Using the values calculated in Table 9 and applying Equation (7), ranking was performed in ascending order, resulting in the final ordering presented in Table 10.
5.1 Sensitivity and comparative analysis
Sensitivity Analysis is an important step in the application of MCDA methods, in order to validate the obtained results. The SPOTIS method was assessed by Azevêdo Junior et al. (2023) and Więckowski et al. (2022), presenting a robust performance when compared to other approaches, credited to the characteristic of ranking reversal resistance.
As stated by Radovanović et al. (2023, 2024), in this paper the sensitivity analysis is followed by the evaluation of the impact of criteria weights changing. This approach implies that the weight vector
This analysis is built on six scenarios. The scenario S0 comprehends the original weights at Table 6; the scenario S1 assesses an equal weights scenario; and the S2 to S5 scenarios evaluate the predominance of one criterion over the others. Table 11 depicts the weights applied in the sensitivity analysis.
Applying the coefficients in the Table 11, the obtained rankings of alternatives are shown in Table 12.
The deviation obtained in the Table 12 suggests that the initial and final ranked alternatives in Cat EB 0 did not suffer major ranking changes (1st to 5th positions and 12th to 18th). In Cat EB 2, there was more ranking fluctuation, but with a minor magnitude, except for the Horlytsya UAV model. The overall behavior of the ranking is depicted in Figure 10.
Applying the Spearman’s correlation coefficient (Table 13), it can be stated that the difference between the scenarios does not represent a major change in the decision-making process. The obtained values are above 0.8, which is considered satisfactory and reinforces that the MPSI-SPOTIS approach is suitable for the research problem context.
For a Comparative Analysis, TOPSIS (Zavadskas et al., 2016), Gaussian AHP (Santos et al., 2021), and MEREC-WISP (Keshavarz-Ghorabaee et al., 2021; Stanujkic et al., 2021) were chosen since they are consolidated MCDA methods in the literature. Table 14 depicts the ranking obtained with these methods.
Assessing the correlation by the Spearman’s coefficient (Table 15), no relevant differences were found between MPSI-SPOTIS and TOPSIS, AHP-G, and MEREC-WISP, since the coefficients obtained were above 0.8.
6. Results
The application of the MPSI-SPOTIS method identified the UAV model Raybird-3 as the most suitable in Category EB 0 and the Searcher (Mk3) model in Category EB 2. Observing the values of the distances
From Figure 11, there is a greater dispersion, with only 4 models falling within the interval
For Category EB 2 (Figure 12), there was a greater concentration of models in the clusters, representing a more balanced division. This facilitates decision-making as there are more options with similar performance in the evaluation. The median of distances found was 0.6310, indicating a greater centralization of alternatives.
From the analysis conducted in the paper, it was identified that the recommended SARP model in Category EB 0 is the Raybird-3, while for Category EB 2, the Searcher (Mk3) model stood out. However, it is emphasized that the evaluation of cost was not possible due to the unavailability of data from the used source (Gettinger, 2019). The clustering proposal suggested the possibility of considering other models beyond those highlighted. For Category EB 0, the Orbiter 3 model proved to be relevant. In Category EB 2, the Bayraktar – TB2 and Seeker 400 models were identified as alternatives to be considered.
7. Conclusion
In this paper, the evaluation of SARP systems was performed by the MCDA method MPSI-SPOTIS. The MPSI method proved to be an adequate approach to obtain the weight vectors, reinforced by the sensitivity and comparative analysis performed. The ranking of SARPs was performed by the SPOTIS method, which presents the feature of rank reversal resistance and adds to the decision-making process the capability to assess new alternatives or remove some without changing the overall scenario.
The sensitivity and comparative analysis performed showed that the weights obtained in MPSI methods satisfactorily described the importance grade of each criterion. Even when some criteria were privileged, the overall ranking did not suffer major changes in the top and bottom alternatives. It was observed that there was a higher deviation only in the middle of the ranking. The SPOTIS method performed statistically equivalent to other established methods, as described by the Spearman’s coefficients above 0.8. Therefore, SPOTIS presents the rank reversal feature and stability, which have been considered suitable to assess defense systems.
It is recommended that future research incorporate cost as an additional criterion in the analysis. Furthermore, the evaluation of criteria related to the life cycle of SARPs can provide a more comprehensive view, including aspects of maintenance, updates, and sustainability.
Figures
Figure 1
Components of a SARP
Figure 2
Rich picture of SARP selection for a deployment
Figure 3
Search steps
Figure 4
Research field overview
Figure 5
Publications per year
Figure 6
Most relevant authors
Figure 7
Most relevant countries
Figure 8
Most relevant documents of SPOTIS method
Figure 9
MPSI-SPOTIS steps
Figure 10
Alternatives rankings network in different scenarios
Figure 11
Category EB 0 clusters
Figure 12
Category EB 2 clusters
SARP categories
| Class | Category EB | Payload (kg) | Deployment force | Deployment level |
|---|---|---|---|---|
| III | 5 | >600 | MD/EMCFA | Strategic |
| III | 4 | C Cj/FTC | Operational | |
| II | 3 | 150–600 | FTC (Division/C Ex) | Tactical |
| I | 2 | 15–150 | Brigade/Division | |
| 1 | <15 | U/Brigade (Light) | ||
| 0 | <10 | até SU |
Source(s): Table adapted from Brazil (2023)
Relevant sources
| Sources | Documents | H index |
|---|---|---|
| Procedia Computer Science | 2 | 109 |
| Expert Systems with Applications | 1 | 249 |
| Information Sciences | 1 | 210 |
| IEEE Access | 1 | 204 |
| Computers and Industrial Engineering | 1 | 148 |
| Industrial Management and Data Systems | 1 | 117 |
| Soft Computing | 1 | 102 |
| Mathematical Problems in Engineering | 1 | 78 |
| Complexity | 1 | 72 |
| Advances in Intelligent Systems and Computing | 1 | 58 |
Source(s): Table by authors
Most relevant documents
| Document | Total citations (TC) | TC mean/year | Normalized TC |
|---|---|---|---|
| RAJ A, 2019, COMPUT IND ENG | 73 | 14.60 | 1.00 |
| SIMIĆ V, 2021, EUR TRANSP RES REV | 45 | 15.00 | 2.74 |
| MOREIRA ML, 2020, SPRINGER PROC MATH STAT | 42 | 10.50 | 1.81 |
| MOREIRA ML, 2021, PROCEDIA COMPUT SCI | 39 | 13.00 | 2.37 |
| HAMURCU M, 2020, J MATH | 33 | 8.25 | 1.42 |
| TOPALOGLU M, 2018, SOFT COMPUT | 28 | 4.67 | 1.87 |
| KOVAČ M, 2021, COMPLEXITY | 22 | 7.33 | 1.34 |
| PAMUCAR D, 2023, INF SCI | 17 | 17.00 | 2.83 |
| NUR F, 2020, J COMPUT DES ENG | 13 | 3.25 | 0.56 |
| DUKIĆ D, 2022, YUGOSL J OPER RES | 8 | 4.00 | 1.78 |
| MOHAMADI HE, 2021, EXPERT SYS APPL | 7 | 2.33 | 0.43 |
| KARAŞAN A, 2020, ADV INTELL SYS COMPUT | 5 | 1.25 | 0.22 |
| ZHANG JZ, 2023, IND MANAGE DATA SYS | 5 | 5.00 | 0.83 |
| CANETTA L, 2018, INT CONF ENG, TECHNOL INNOV: ENG, TECHNOL INNOV MANAG BEYOND: NEW CHALLENGES, NEW APPROACHES, ICE/ITMC - PROC | 2 | 0.33 | 0.13 |
| SOHAIB KHAN M, 2021, PROC INT BHURBAN CONF APPL SCI TECHNOL, IBCAST | 1 | 0.33 | 0.06 |
Source(s): Table by authors
Assessed models
| Cat EB 0 | Cat EB 2 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Model | Country | Endurance | Range | MTOW | Payload | Model | Country | Endurance | Range | MTOW | Payload |
| Orbiter 3 | Israel | 7 | 150 | 30 | 5.5 | Searcher (Mk 3) | Israel | 18 | 250 | 450 | 120 |
| Ptero-5E | Russia | 8 | 75 | 30 | 5 | Seeker 400 | South Africa | 16 | 250 | 450 | 100 |
| Raybird-3 | Ukraine | 15 | 240 | 21 | 5 | Falco | Italy | 14 | 200 | 490 | 70 |
| FlyEye | Peru | 2.5 | 30 | 11 | 4 | Burevestnik-MB | Belarus | 10 | 290 | 400 | 60 |
| Fulmar X | Spain | 8 | 80 | 20 | 4 | Bayraktar-TB2 | Turkey | 20 | 150 | 630 | 55 |
| FT-100 Horus | Brazil | 2 | 20 | 7 | 3 | CamCopter S-100 | Austria | 10 | 200 | 200 | 50 |
| IT180-3EL-1 | France | 0.8 | 10 | 21 | 3 | ASN-206 | China | 8 | 150 | 222 | 50 |
| Orbiter-1K | Israel | 2.5 | 100 | 11 | 2.6 | ASN-209 | China | 10 | 200 | 320 | 50 |
| MicroFalcon | Israel | 4 | 40 | 10 | 2 | Shahpar | Pakistan | 7 | 250 | 480 | 50 |
| Spectator-M | Ukraine | 2 | 50 | 5.5 | 2 | Horlytsya | Ukraine | 7 | 1,050 | 200 | 50 |
| Observer-S | Ukraine | 5 | 100 | 6.5 | 1.5 | RQ-7 Shadow | USA | 9 | 125 | 212 | 43 |
| Spy'Ranger | France | 3 | 30 | 14.5 | 1.2 | Aludra (Mk1) | Malaysia | 3 | 100 | 200 | 25 |
| Skylark I-LEX | Israel | 3 | 40 | 7.5 | 1.2 | PGZ-19R | Peru | 12 | 150 | 90 | 20 |
| Serçe-1 | Turkey | 0.5 | 3 | 6.5 | 1 | ||||||
| XeFI | South Korea | 0.5 | 3 | 3 | 0.5 | ||||||
| Mosquito | Belarus | 0.75 | 12 | 2.8 | 0.35 | ||||||
| InstantEye Gen3 | USA | 0.5 | 2 | 0.5 | 0.3 | ||||||
| Aladin | Germany | 1 | 15 | 3.2 | 0 | ||||||
Source(s): Table adapted from Gettinger (2019)
Normalized decision matrix
| Cat EB 0 | Cat EB 2 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Objective | Max | Max | Max | Max | Objective | Max | Max | Max | Max | ||
| Model | Country | Endurance | Range | MTOW | Payload | Model | Country | Endurance | Range | MTOW | Payload |
| Orbiter 3 | Israel | 0.4667 | 0.6250 | 1.0000 | 1.0000 | Searcher (Mk 3) | Israel | 0.9000 | 0.2381 | 0.7143 | 1.0000 |
| Ptero-5E | Russia | 0.5333 | 0.3125 | 1.0000 | 0.9091 | Seeker 400 | South Africa | 0.8000 | 0.2381 | 0.7143 | 0.8333 |
| Raybird-3 | Ukraine | 1.0000 | 1.0000 | 0.7000 | 0.9091 | Falco | Italy | 0.7000 | 0.1905 | 0.7778 | 0.5833 |
| FlyEye | Peru | 0.1667 | 0.1250 | 0.3667 | 0.7273 | Burevestnik-MB | Belarus | 0.5000 | 0.2762 | 0.6349 | 0.5000 |
| Fulmar X | Spain | 0.5333 | 0.3333 | 0.6667 | 0.7273 | Bayraktar-TB2 | Turkey | 1.0000 | 0.1429 | 1.0000 | 0.4583 |
| FT-100 Horus | Brazil | 0.1333 | 0.0833 | 0.2333 | 0.5455 | CamCopter S-100 | Austria | 0.5000 | 0.1905 | 0.3175 | 0.4167 |
| IT180-3EL-1 | France | 0.0533 | 0.0417 | 0.7000 | 0.5455 | ASN-206 | China | 0.4000 | 0.1429 | 0.3524 | 0.4167 |
| Orbiter-1K | Israel | 0.1667 | 0.4167 | 0.3667 | 0.4727 | ASN-209 | China | 0.5000 | 0.1905 | 0.5079 | 0.4167 |
| MicroFalcon | Israel | 0.2667 | 0.1667 | 0.3333 | 0.3636 | Shahpar | Pakistan | 0.3500 | 0.2381 | 0.7619 | 0.4167 |
| Spectator-M | Ukraine | 0.1333 | 0.2083 | 0.1833 | 0.3636 | Horlytsya | Ukraine | 0.3500 | 1.0000 | 0.3175 | 0.4167 |
| Observer-S | Ukraine | 0.3333 | 0.4167 | 0.2167 | 0.2727 | RQ-7 Shadow | USA | 0.4500 | 0.1190 | 0.3365 | 0.3583 |
| Spy'Ranger | France | 0.2000 | 0.1250 | 0.4833 | 0.2182 | Aludra (Mk1) | Malaysia | 0.1500 | 0.0952 | 0.3175 | 0.2083 |
| Skylark I-LEX | Israel | 0.2000 | 0.1667 | 0.2500 | 0.2182 | PGZ-19R | Peru | 0.6000 | 0.1429 | 0.1429 | 0.1667 |
| Serçe-1 | Turkey | 0.0333 | 0.0125 | 0.2167 | 0.1818 | ||||||
| XeFI | South Korea | 0.0333 | 0.0125 | 0.1000 | 0.0909 | ||||||
| Mosquito | Belarus | 0.0500 | 0.0500 | 0.0933 | 0.0636 | ||||||
| InstantEye Gen3 | USA | 0.0333 | 0.0083 | 0.0167 | 0.0545 | ||||||
| Aladin | Germany | 0.0667 | 0.0625 | 0.1067 | 0.0000 | ||||||
Source(s): Table by authors
Value of vectors
| Cat EB | – | Endurance | Range | MTOW | Payload | Cat EB | Endurance | Range | MTOW | Payload |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.2446 | 0.2315 | 0.3907 | 0.4258 | 2 | 0.5538 | 0.2465 | 0.5304 | 0.4763 | |
| 1.0759 | 1.1278 | 1.5647 | 1.7488 | 0.6923 | 0.6496 | 0.7699 | 0.6135 | |||
| 0.1950 | 0.2044 | 0.2836 | 0.3170 | 0.2540 | 0.2384 | 0.2825 | 0.2251 |
Source(s): Table by authors
Values of
| Cat EB 0 | Cat EB 2 | |||||
|---|---|---|---|---|---|---|
| ISP | ISP | |||||
| 0.5 | 15 | 15 | 3 | 20 | 20 | |
| 2 | 240 | 240 | 100 | 1,050 | 1,050 | |
| 0.5 | 30 | 30 | 90 | 630 | 630 | |
| 0 | 5.5 | 5.5 | 20 | 120 | 120 | |
Source(s): Table by authors
Normalized distance matrix
| Cat EB 0 | Cat EB 2 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Model | Country | Endurance | Range | MTOW | Payload | Model | Country | Endurance | Range | MTOW | Payload |
| Orbiter 3 | Israel | 0.5517 | 0.3782 | 0.0000 | 0.0000 | Searcher (Mk 3) | Israel | 0.1176 | 0.8421 | 0.3333 | 0.0000 |
| Ptero-5E | Russia | 0.4828 | 0.6933 | 0.0000 | 0.0909 | Seeker 400 | South Africa | 0.2353 | 0.8421 | 0.3333 | 0.2000 |
| Raybird-3 | Ukraine | 0.0000 | 0.0000 | 0.3051 | 0.0909 | Falco | Italy | 0.3529 | 0.8947 | 0.2593 | 0.5000 |
| FlyEye | Peru | 0.8621 | 0.8824 | 0.6441 | 0.2727 | Burevestnik-MB | Belarus | 0.5882 | 0.8000 | 0.4259 | 0.6000 |
| Fulmar X | Spain | 0.4828 | 0.6723 | 0.3390 | 0.2727 | Bayraktar-TB2 | Turkey | 0.0000 | 0.9474 | 0.0000 | 0.6500 |
| FT-100 Horus | Brazil | 0.8966 | 0.9244 | 0.7797 | 0.4545 | CamCopter S-100 | Austria | 0.5882 | 0.8947 | 0.7963 | 0.7000 |
| IT180-3EL-1 | France | 0.9793 | 0.9664 | 0.3051 | 0.4545 | ASN-206 | China | 0.7059 | 0.9474 | 0.7556 | 0.7000 |
| Orbiter-1K | Israel | 0.8621 | 0.5882 | 0.6441 | 0.5273 | ASN-209 | China | 0.5882 | 0.8947 | 0.5741 | 0.7000 |
| MicroFalcon | Israel | 0.7586 | 0.8403 | 0.6780 | 0.6364 | Shahpar | Pakistan | 0.7647 | 0.8421 | 0.2778 | 0.7000 |
| Spectator-M | Ukraine | 0.8966 | 0.7983 | 0.8305 | 0.6364 | Horlytsya | Ukraine | 0.7647 | 0.0000 | 0.7963 | 0.7000 |
| Observer-S | Ukraine | 0.6897 | 0.5882 | 0.7966 | 0.7273 | RQ-7 Shadow | USA | 0.6471 | 0.9737 | 0.7741 | 0.7700 |
| Spy'Ranger | France | 0.8276 | 0.8824 | 0.5254 | 0.7818 | Aludra (Mk1) | Malaysia | 1.0000 | 1.0000 | 0.7963 | 0.9500 |
| Skylark I-LEX | Israel | 0.8276 | 0.8403 | 0.7627 | 0.7818 | PGZ-19R | Peru | 0.4706 | 0.9474 | 1.0000 | 1.0000 |
| Serçe-1 | Turkey | 1.0000 | 0.9958 | 0.7966 | 0.8182 | ||||||
| XeFI | South Korea | 1.0000 | 0.9958 | 0.9153 | 0.9091 | ||||||
| Mosquito | Belarus | 0.9828 | 0.9580 | 0.9220 | 0.9364 | ||||||
| InstantEye Gen3 | USA | 1.0000 | 1.0000 | 1.0000 | 0.9455 | ||||||
| Aladin | Germany | 0.9655 | 0.9454 | 0.9085 | 1.0000 | ||||||
Source(s): Table by authors
Normalized average distance matrix
| Cat EB 0 | Cat EB 2 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Model | Country | Endurance | Range | MTOW | Payload | Model | Country | Endurance | Range | MTOW | Payload |
| Orbiter 3 | Israel | 0.1076 | 0.0773 | 0.0000 | 0.0000 | Searcher (Mk 3) | Israel | 0.0299 | 0.2008 | 0.0942 | 0.0000 |
| Ptero-5E | Russia | 0.0941 | 0.1417 | 0.0000 | 0.0288 | Seeker 400 | South Africa | 0.0598 | 0.2008 | 0.0942 | 0.0450 |
| Raybird-3 | Ukraine | 0.0000 | 0.0000 | 0.0865 | 0.0288 | Falco | Italy | 0.0896 | 0.2133 | 0.0732 | 0.1126 |
| FlyEye | Peru | 0.1681 | 0.1804 | 0.1827 | 0.0865 | Burevestnik-MB | Belarus | 0.1494 | 0.1907 | 0.1203 | 0.1351 |
| Fulmar X | Spain | 0.0941 | 0.1374 | 0.0961 | 0.0865 | Bayraktar-TB2 | Turkey | 0.0000 | 0.2259 | 0.0000 | 0.1463 |
| FT-100 Horus | Brazil | 0.1748 | 0.1889 | 0.2211 | 0.1441 | CamCopter S-100 | Austria | 0.1494 | 0.2133 | 0.2250 | 0.1576 |
| IT180-3EL-1 | France | 0.1910 | 0.1975 | 0.0865 | 0.1441 | ASN-206 | China | 0.1793 | 0.2259 | 0.2134 | 0.1576 |
| Orbiter-1K | Israel | 0.1681 | 0.1202 | 0.1827 | 0.1671 | ASN-209 | China | 0.1494 | 0.2133 | 0.1622 | 0.1576 |
| MicroFalcon | Israel | 0.1479 | 0.1718 | 0.1923 | 0.2017 | Shahpar | Pakistan | 0.1942 | 0.2008 | 0.0785 | 0.1576 |
| Spectator-M | Ukraine | 0.1748 | 0.1632 | 0.2355 | 0.2017 | Horlytsya | Ukraine | 0.1942 | 0.0000 | 0.2250 | 0.1576 |
| Observer-S | Ukraine | 0.1345 | 0.1202 | 0.2259 | 0.2305 | RQ-7 Shadow | USA | 0.1644 | 0.2321 | 0.2187 | 0.1733 |
| Spy'Ranger | France | 0.1614 | 0.1804 | 0.1490 | 0.2478 | Aludra (Mk1) | Malaysia | 0.2540 | 0.2384 | 0.2250 | 0.2138 |
| Skylark I-LEX | Israel | 0.1614 | 0.1718 | 0.2163 | 0.2478 | PGZ-19R | Peru | 0.1195 | 0.2259 | 0.2825 | 0.2251 |
| Serçe-1 | Turkey | 0.1950 | 0.2035 | 0.2259 | 0.2594 | ||||||
| XeFI | South Korea | 0.1950 | 0.2035 | 0.2596 | 0.2882 | ||||||
| Mosquito | Belarus | 0.1916 | 0.1958 | 0.2615 | 0.2968 | ||||||
| InstantEye Gen3 | USA | 0.1950 | 0.2044 | 0.2836 | 0.2997 | ||||||
| Aladin | Germany | 0.1883 | 0.1932 | 0.2576 | 0.3170 | ||||||
Source(s): Table by authors
Final ranking
| Cat EB 0 | Cat EB 2 | ||||||
|---|---|---|---|---|---|---|---|
| Model | Country | Rank | Model | Country | Rank | ||
| Raybird-3 | Ukraine | 0.1153 | 1º | Searcher (Mk 3) | Israel | 0.3248 | 1º |
| Orbiter 3 | Israel | 0.1849 | 2º | Bayraktar-TB2 | Turkey | 0.3722 | 2º |
| Ptero-5E | Russia | 0.2647 | 3º | Seeker 400 | South Africa | 0.3997 | 3º |
| Fulmar X | Spain | 0.4141 | 4º | Falco | Italy | 0.4887 | 4º |
| FlyEye | Peru | 0.6176 | 5º | Horlytsya | Ukraine | 0.5768 | 5º |
| IT180-3EL-1 | France | 0.6191 | 6º | Burevestnik-MB | Belarus | 0.5955 | 6º |
| Orbiter-1K | Israel | 0.6381 | 7º | Shahpar | Pakistan | 0.6310 | 7º |
| Observer-S | Ukraine | 0.7112 | 8º | ASN-209 | China | 0.6825 | 8º |
| MicroFalcon | Israel | 0.7137 | 9º | CamCopter S-100 | Austria | 0.7452 | 9º |
| FT-100 Horus | Brazil | 0.7290 | 10º | ASN-206 | China | 0.7762 | 10º |
| Spy'Ranger | France | 0.7386 | 11º | RQ-7 Shadow | USA | 0.7885 | 11º |
| Spectator-M | Ukraine | 0.7753 | 12º | PGZ-19R | Peru | 0.8530 | 12º |
| Skylark I-LEX | Israel | 0.7973 | 13º | Aludra (Mk1) | Malaysia | 0.9312 | 13º |
| Serçe-1 | Turkey | 0.8838 | 14º | ||||
| Mosquito | Belarus | 0.9458 | 15º | ||||
| XeFI | South Korea | 0.9463 | 16º | ||||
| Aladin | Germany | 0.9562 | 17º | ||||
| InstantEye Gen3 | USA | 0.9827 | 18º | ||||
Source(s): Table by authors
Weight coefficients in sensitivity analysis
| Cat EB | Criteria | Scenarios | |||||
|---|---|---|---|---|---|---|---|
| S0 | S1 | S2 | S3 | S4 | S5 | ||
| Cat EB 0 | C1 | 0.1950 | 0.2500 | 0.4000 | 0.2000 | 0.2000 | 0.2000 |
| C2 | 0.2044 | 0.2500 | 0.2000 | 0.4000 | 0.2000 | 0.2000 | |
| C3 | 0.2836 | 0.2500 | 0.2000 | 0.2000 | 0.4000 | 0.2000 | |
| C4 | 0.3170 | 0.2500 | 0.2000 | 0.2000 | 0.2000 | 0.4000 | |
| Cat EB 2 | C1 | 0.2540 | 0.2500 | 0.4000 | 0.2000 | 0.2000 | 0.2000 |
| C2 | 0.2384 | 0.2500 | 0.2000 | 0.4000 | 0.2000 | 0.2000 | |
| C3 | 0.2825 | 0.2500 | 0.2000 | 0.2000 | 0.4000 | 0.2000 | |
| C4 | 0.2251 | 0.2500 | 0.2000 | 0.2000 | 0.2000 | 0.4000 | |
Source(s): Table by authors
Alternatives rankings in different scenarios
| Cat EB 0 | Cat EB 2 | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alternatives | Scenarios | Alternatives | Scenarios | ||||||||||||
| S0 | S1 | S2 | S3 | S4 | S5 | S0 | S1 | S2 | S3 | S4 | S5 | ||||
| Raybird-3 | 1 | 1 | 1 | 1 | 1 | 1 | 0.0 | Searcher (Mk 3) | 1 | 1 | 1 | 1 | 2 | 1 | 0.4 |
| Orbiter 3 | 2 | 2 | 2 | 2 | 2 | 2 | 0.0 | Bayraktar-TB2 | 2 | 2 | 2 | 4 | 1 | 3 | 1.0 |
| Ptero-5E | 3 | 3 | 3 | 3 | 3 | 3 | 0.0 | Seeker 400 | 3 | 3 | 3 | 3 | 3 | 2 | 0.4 |
| Fulmar X | 4 | 4 | 4 | 4 | 4 | 4 | 0.0 | Falco | 4 | 4 | 4 | 5 | 4 | 4 | 0.4 |
| FlyEye | 5 | 6 | 7 | 7 | 7 | 5 | 1.0 | Horlytsya | 5 | 5 | 6 | 2 | 7 | 5 | 1.7 |
| IT180-3EL-1 | 6 | 7 | 9 | 8 | 5 | 7 | 1.4 | Burevestnik-MB | 6 | 6 | 5 | 6 | 5 | 6 | 0.5 |
| Orbiter-1K | 7 | 5 | 5 | 5 | 6 | 6 | 0.8 | Shahpar | 7 | 7 | 8 | 7 | 6 | 7 | 0.6 |
| Observer-S | 8 | 8 | 6 | 6 | 10 | 9 | 1.6 | ASN-209 | 8 | 8 | 7 | 8 | 8 | 8 | 0.4 |
| MicroFalcon | 9 | 9 | 8 | 9 | 9 | 10 | 0.6 | CamCopter S-100 | 9 | 9 | 9 | 9 | 9 | 9 | 0.0 |
| FT-100 Horus | 10 | 11 | 11 | 12 | 11 | 8 | 1.4 | ASN-206 | 10 | 10 | 11 | 10 | 10 | 10 | 0.4 |
| Spy'Ranger | 11 | 10 | 10 | 10 | 8 | 12 | 1.3 | RQ-7 Shadow | 11 | 11 | 10 | 11 | 11 | 11 | 0.4 |
| Spectator-M | 12 | 12 | 13 | 11 | 13 | 11 | 0.9 | PGZ-19R | 12 | 12 | 12 | 12 | 12 | 12 | 0.0 |
| Skylark I-LEX | 13 | 13 | 12 | 13 | 12 | 13 | 0.5 | Aludra (Mk1) | 13 | 13 | 13 | 13 | 13 | 13 | 0.0 |
| Serçe-1 | 14 | 14 | 14 | 14 | 14 | 14 | 0.0 | ||||||||
| Mosquito | 15 | 15 | 15 | 15 | 15 | 16 | 0.4 | ||||||||
| XeFI | 16 | 17 | 17 | 17 | 17 | 15 | 0.8 | ||||||||
| Aladin | 17 | 16 | 16 | 16 | 16 | 17 | 0.5 | ||||||||
| InstantEye Gen3 | 18 | 18 | 18 | 18 | 18 | 18 | 0.0 | ||||||||
Source(s): Table by authors
Spearman’s correlation of scenarios
| Cat EB 0 | Cat EB 2 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S0 | S1 | S2 | S3 | S4 | S5 | S0 | S1 | S2 | S3 | S4 | S5 | ||
| S0 | 1 | S0 | 1 | ||||||||||
| S1 | 0.990 | 1 | S1 | 1.000 | 1 | ||||||||
| S2 | 0.971 | 0.988 | 1 | S2 | 0.984 | 0.984 | 1 | ||||||
| S3 | 0.975 | 0.992 | 0.992 | 1 | S3 | 0.962 | 0.962 | 0.929 | 1 | ||||
| S4 | 0.975 | 0.983 | 0.961 | 0.963 | 1 | S4 | 0.978 | 0.978 | 0.973 | 0.896 | 1 | ||
| S5 | 0.988 | 0.975 | 0.953 | 0.957 | 0.953 | 1 | S5 | 0.995 | 0.995 | 0.978 | 0.967 | 0.967 | 1 |
Source(s): Table by authors
Comparative analysis of MPSI-SPOTIS
| Alternatives | MPSI-SPOTIS | TOPSIS | AHP-G | MEREC-WISP | Alternatives | MPSI-SPOTIS | TOPSIS | AHP-G | MEREC-WISP | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Raybird-3 | 1 | 1 | 1 | 1 | 0.0 | Searcher (Mk 3) | 1 | 2 | 2 | 1 | 0.6 |
| Orbiter 3 | 2 | 2 | 2 | 2 | 0.0 | Bayraktar-TB2 | 2 | 4 | 4 | 3 | 1.0 |
| Ptero-5E | 3 | 3 | 3 | 3 | 0.0 | Seeker 400 | 3 | 3 | 3 | 4 | 0.5 |
| Fulmar X | 4 | 4 | 4 | 4 | 0.0 | Falco | 4 | 5 | 5 | 5 | 0.5 |
| FlyEye | 5 | 6 | 7 | 7 | 1.0 | Horlytsya | 5 | 1 | 1 | 2 | 1.9 |
| IT180-3EL-1 | 6 | 5 | 10 | 5 | 2.4 | Burevestnik-MB | 6 | 6 | 6 | 9 | 1.5 |
| Orbiter-1K | 7 | 7 | 6 | 6 | 0.6 | Shahpar | 7 | 7 | 7 | 10 | 1.5 |
| Observer-S | 8 | 8 | 5 | 8 | 1.5 | ASN-209 | 8 | 8 | 8 | 8 | 0.0 |
| MicroFalcon | 9 | 10 | 8 | 9 | 0.8 | CamCopter S-100 | 9 | 9 | 9 | 7 | 1.0 |
| FT-100 Horus | 10 | 9 | 13 | 10 | 1.7 | ASN-206 | 10 | 10 | 10 | 6 | 2.0 |
| Spy'Ranger | 11 | 11 | 9 | 11 | 1.0 | RQ-7 Shadow | 11 | 12 | 11 | 11 | 0.5 |
| Spectator-M | 12 | 12 | 11 | 12 | 0.5 | PGZ-19R | 12 | 11 | 12 | 13 | 0.8 |
| Skylark I-LEX | 13 | 13 | 12 | 13 | 0.5 | Aludra (Mk1) | 13 | 13 | 13 | 12 | 0.5 |
| Serçe-1 | 14 | 14 | 14 | 14 | 0.0 | ||||||
| Mosquito | 15 | 17 | 16 | 15 | 1.0 | ||||||
| XeFI | 16 | 15 | 17 | 16 | 0.8 | ||||||
| Aladin | 17 | 16 | 15 | 17 | 1.0 | ||||||
| InstantEye Gen3 | 18 | 18 | 18 | 18 | 0.0 |
Source(s): Table by authors
Spearman’s correlation of methods
| Cat EB 0 | Cat EB 2 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| MPSI-SPOTIS | TOPSIS | AHP-G | MEREC-WISP | MPSI-SPOTIS | TOPSIS | AHP-G | MEREC-WISP | ||
| MPSI-SPOTIS | 1 | MPSI-SPOTIS | 1 | ||||||
| TOPSIS | 0.990 | 1 | TOPSIS | 0.934 | 1 | ||||
| AHP-G | 0.946 | 0.930 | 1 | AHP-G | 0.940 | 0.995 | 1 | ||
| MEREC-WISP | 0.994 | 0.990 | 0.942 | 1 | MEREC-WISP | 0.857 | 0.868 | 0.879 | 1 |
Source(s): Table by authors
References
Akram, M., Noreen, U. and Deveci, M. (2024), “An outranking method for optimizing anti-aircraft missile system with 2-tuple linguistic m-polar fuzzy data”, Engineering Applications of Artificial Intelligence, Vol. 132, 107923, doi: 10.1016/j.engappai.2024.107923.
Al-Hchaimi, A.A.J., Sulaiman, N.B., Mustafa, M.A.B., Mohtar, M.N.B., Mohd Hassan, S.L.B. and Muhsen, Y.R. (2023), “A comprehensive evaluation approach for efficient countermeasure techniques against timing side-channel attack on MPSoC-based IoT using multi-criteria decision-making methods”, Egyptian Informatics Journal, Vol. 24 No. 2, pp. 351-364, doi: 10.1016/j.eij.2023.05.005.
Aria, M. and Cuccurullo, C. (2017), “Bibliometrix: an R-tool for comprehensive science mapping analysis”, Journal of Infometrics, Vol. 11 No. 4, pp. 959-975, doi: 10.1016/j.joi.2017.08.007.
Azevêdo Junior, C.M., Júnior, E.L.P., Araujo, T.M.P.D.C., dos Santos, M., Gomes, C.F.S. and Pereira, D.A.D.M. (2023), “Ordering of solar photovoltaic panels using the MEREC-SPOTIS hybrid analytical model”, Procedia Computer Science, Vol. 230, pp. 808-818, doi: 10.1016/j.procs.2023.12.052.
Belal, M.M. and Sundaram, D.M. (2023), “An intelligent protection framework for intrusion detection in cloud environment based on covariance matrix self-adaptation evolution strategy and multi-criteria decision-making”, Journal of Intelligent & Fuzzy Systems, Vol. 44 No. 6, pp. 8971-9001, doi: 10.3233/JIFS-224135.
BRAZIL. Exército Brasileiro (2023), “Normas Operacionais de Emprego dos Sistemas de Aeronaves Remotamente Pilotadas e das ARP não SMEM”, available at: http://www.sgex.eb.mil.br/sistemas/boletim_do_exercito/copiar.php?codarquivo=181261607&act=sep (accessed 5 November 2023).
BRAZIL. Ministério da Defesa (2017), “Aprova a Compreensão das Operações (COMOP) nº 08/2017, do Sistema Integrado de Monitoramento de Fronteiras (SISFRON)”, Portaria nº 462-EME, de 21 de novembro 2017, available at: http://www.sgex.eb.mil.br/sg8/006_outras_publicacoes/07_publicacoes_diversas/04_estado_maior_do_exercito/port_n_462_eme_21nov2017.html (accessed 10 October 2023).
BRAZIL Exército Brasileiro (2020b), “Aprova os Requisitos Operacionais do Sistema de Sensoriamento e Apoio à Decisão da Fase 3A do Programa Estratégico do Exército Sistema Integrado de Monitoramento de Fronteiras (EB20-RO-04.065)”, Portaria nº 045-EME, de 12 de fevereiro de 2020 (APROVAÇÃO), available at: http://www.sgex.eb.mil.br/sg8/006_outras_publicacoes/09_requisitos_operacionais/port_n_045_eme_12fev2020.html (accessed 10 October 2023).
BRAZIL Exército Brasileiro (2020c), “Aprova a Diretriz de Iniciação do Projeto Sistemas de Aeronaves Remotamente Pilotadas e cria a Equipe para a realização do Estudo de Viabilidade para o Projeto (EB20-D-08.042)”, Portaria nº 093 – EME, de 14 de Maio de 2020, available at: http://www.sgex.eb.mil.br/sistemas/boletim_do_exercito/copiar.php?codarquivo=1792&act=bre (accessed 5 November 2023).
BRAZIL Ministério da Defesa (2020a), “Política Nacional de Defesa e Estratégia Nacional de Defesa”, available at: https://www.gov.br/defesa/pt-br/arquivos/estado_e_defesa/pnd_end_congresso_.pdf (accessed 19 August 2023).
BRAZIL. Presidência da República (1999), “Dispõe sobre as normas gerais para a organização, o preparo e o emprego das Forças Armadas. Lei Complementar nº 97, de 9 de junho de”, available at: https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp97.htm#:∼:text=LEI%20COMPLEMENTAR%20N%C2%BA%2097%2C%20DE%209%20DE%20JUNHO%20DE%201999&text=Disp%C3%B5e%20sobre%20as%20normas%20gerais,o%20emprego%20das%20For%C3%A7as%20Armadas (accessed 5 November 2023).
Canetta, L., Mattei, G. and Guanziroli, A. (2018), “Multi criteria analysis applied on value chain definition in unmanned aerial vehicle (UAV) sector”, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), Madeira, 2018, pp. 1096-1103, doi: 10.1109/ICE.2017.8280004.
Desticioglu Tasdemir, B. and Asilogullari Ayan, M. (2024), “Sustainable supplier selection in the defense industry with multi-criteria decision-making methods”, in Advances in Intelligent Manufacturing and Service System Informatics IMSS 2023, pp. 95-106, doi: 10.1007/978-981-99-6062-0_10.
Dezert, J., Tchamova, A., Han, D. and Tacnet, J. (2020), “The SPOTIS rank reversal free method for multi-criteria decision-making support”, 2020 IEEE 23rd International Conference on Information Fusion (FUSION), IEEE, Rustenburg, pp. 1-8. doi: 10.23919/FUSION45008.2020.9190347.
Dukić, D., Petrovic, I., Bozanic, D. and Delibasic, B. (2022), “Selection of unployed aircraft for training of small-range aircraft defense system AHP - TOPSIS optimization methods”, Yugoslav Journal of Operations Research, Vol. 32 No. 3, pp. 389-406, doi: 10.2298/YJOR211125007D.
Gavião, L.O., Lima, G.B.A., Garcia, P.A.D.A. and Teixeira, L.D.S. (2024), “Decision support based on performance data using the analytic hierarchy process without expert judgement”, Brazilian Journal of Operations & Production Management, Vol. 21 No. 1, p. 1882, doi: 10.14488/BJOPM.1882.2024.
Gettinger, D. (2019), The Drone Databook, Bard College, New York, NY.
Girardi, R. and Santos, M.D. (2023), “Evaluation of technologies to the Brazilian army from the application of the AHP-TOPSIS-2N method with patent analysis”, 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp. 1-6, doi: 10.1109/ICECCME57830.2023.10252404.
Girardi, R., Galdino, J.F., Pellanda, P.C., Junior, J.A.F. and dos Santos, M. (2022), “Bibliometric study on multi-criteria decision-making methods applied to life cycle management of defense systems”, Procedia Computer Science, Vol. 214, pp. 236-247, doi: 10.1016/j.procs.2022.11.171.
Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M. and Langović, Z. (2022), “Novel hybrid MPSI–MARA decision-making model for support system selection in an underground mine”, Systems, Vol. 10 No. 248, pp. 1-21, doi: 10.3390/systems10060248.
Güneri, B. and Deveci, M. (2023), “Evaluation of supplier selection in the defense industry using q-rung orthopair fuzzy set based EDAS approach”, Expert Systems with Applications, Vol. 222, 119846, doi: 10.1016/j.eswa.2023.119846.
Hamurcu, M. and Eren, T. (2020), “Selection of unmanned aerial vehicles by using multicriteria decision-making for defence”, Journal of Mathematics, Vol. 2020, pp. 1-11, doi: 10.1155/2020/4308756.
Karaşan, A. and Kaya, İ. (2020), “Neutrosophic TOPSIS method for technology evaluation of unmanned aerial vehicles (UAVs)”, Advances in Intelligent Systems and Computing, Vol. 1029, pp. 665-673, doi: 10.1007/978-3-030-23756-1_80.
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z. and Antucheviciene, J. (2021), “Determination of objective weights using a new method based on the removal effects of criteria (MEREC)”, Symmetry, Vol. 13 No. 4, p. 525, doi: 10.3390/sym13040525.
Kovač, M., Tadić, S., Krstić, M. and Bouraima, M.B. (2021), “Novel spherical fuzzy MARCOS method for assessment of drone-based city logistics concepts”, Complexity, Vol. 2021, pp. 1-17, doi: 10.1155/2021/2374955.
Kurnaz, S., Özdağoğlu, A. and Keleş, M.K. (2023), “Method of evaluation of military helicopter pilot selection criteria: a novel Grey SWARA approach”, Aviation, Vol. 27 No. 1, pp. 27-35, doi: 10.3846/aviation.2023.18596.
Litmaps (n.d.), “Literature map software for literature reviews & research”, available at: https://www.litmaps.com/ (accessed 7 November 2023).
Mohamadi, H.E., Kara, N. and Lagha, M. (2021), “Efficient algorithms for decision making and coverage deployment of connected multi-low-altitude platforms”, Expert Systems with Applications, Vol. 184, 115529, doi: 10.1016/j.eswa.2021.115529.
Moreira, M.Â.L., Gomes, C.F.S., dos Santos, M., do Carmo Silva, M. and Araujo, J.V.G.A. (2020), “PROMETHEE-SAPEVO-M1 a hybrid modeling proposal: multicriteria evaluation of drones for use in naval Warfare”, Springer Proceedings in Mathematics and Statistics, Vol. 337, pp. 381-393, doi: 10.1007/978-3-030-56920-4_31.
Moreira, M.Â.L., Gomes, C.F.S., Santos, M.D, Basilio, M.P., Costa, I.P.D.A., Rocha Junior, C.D.S. and Jardim, R.R.A.J. (2021), “Evaluation of drones for public security: a multicriteria approach by the PROMETHEE-SAPEVO-M1 systematic”, Procedia Computer Science, Vol. 199, pp. 125-133, doi: 10.1016/j.procs.2022.01.016.
Moreira, M.Â.L., Silva, F.C.A., de Araújo Costa, I.P., Gomes, C.F.S. and Santos, M.d. (2023), “SAPEVO-H2 a multi-criteria systematic based on a hierarchical structure: decision-making analysis for assessing anti-RPAS strategies in sensing environments”, Processes, Vol. 11 No. 2, p. 352, doi: 10.3390/pr11020352.
Nur, F., Alrahahleh, A., Burch, R., Babski-Reeves, K. and Marufuzzaman, M. (2020), “Last mile delivery drone selection and evaluation using the interval-valued inferential fuzzy TOPSIS”, Journal of Computational Design and Engineering, Vol. 7 No. 4, pp. 397-411, doi: 10.1093/jcde/qwaa033.
Pamucar, D., Gokasar, I., Ebadi Torkayesh, A., Deveci, M., Martínez, L. and Wu, Q. (2023), “Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the Hamacher operator”, Information Sciences, Vol. 622, pp. 374-404, doi: 10.1016/j.ins.2022.11.143.
Radovanović, M., Petrovski, A., Behlić, A., Perišić, M., Samopjan, M. and Lakanović, B. (2023), “Application model of MCDM for selection of automatic rifle”, Journal of Decision Analytics and Intelligent Computing, Vol. 3 No. 1, pp. 185-196, doi: 10.31181/jdaic10011102023r.
Radovanović, M., Petrovski, A., Cirkin, E., Behlić, A., Jokić, Ž., Chemezov, D., Hashimov, E.G. Bouraima, M.B. and Jana, C. (2024), “Application of the new hybrid model LMAW-GEDAS multi-criteria decision-making when choosing an assault rifle for the needs of the army”, Journal of Decision Analytics and Intelligent Computing, Vol. 4 No. 1, pp. 16-31, doi: 10.31181/jdaic10021012024r.
Raj, A. and Sah, B. (2019), “Analyzing critical success factors for implementation of drones in the logistics sector using grey-DEMATEL based approach”, Computers and Industrial Engineering, Vol. 138, 106118, doi: 10.1016/j.cie.2019.106118.
Rajurkar, K.S., Khade, S.P., Sawant, O.M., Dobe, M.P. and Kamble, A.G. (2023), “Selection of fighter aircraft by using multi attributes decision making methods”, International Journal of Process Management and Benchmarking, Vol. 14 No. 4, pp. 460-477, doi: 10.1504/IJPMB.2023.132217.
Rasmussen, A., Sabic, H., Saha, S. and Nielsen, I.E. (2023), “Supplier selection for aerospace & defense industry through MCDM methods”, Cleaner Engineering and Technology, Vol. 12, 100590, doi: 10.1016/j.clet.2022.100590.
Santos, M., Costa, I.P.d.A. and Gomes, C.F.S. (2021), “Multicriteria decision making in the selection of warships: a new approach to the AHP method”, International Journal of the Analytic Hierarchy Process, Vol. 13 No. 1, pp. 147-169, doi: 10.13033/ijahp.v13i1.833.
Simić, V., Lazarević, D. and Dobrodolac, M. (2021), “Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade”, European Transport Research Review, Vol. 13 No. 1, 43, doi: 10.1186/s12544-021-00501-6.
Stanujkic, D., Popovic, G., Karabasevic, D., Meidute-Kavaliauskiene, I. and Ulutaş, A. (2021), “An integrated simple weighted sum product method – WISP”, IEEE Transactions on Engineering Management, Vol. 70 No. 5, pp. 1933-1944, doi: 10.1109/TEM.2021.3075783.
Tešić, D., Božanić, D., Stojković, D., Puška, A. and Stojanović, I. (2023), “DIBR–DOMBI–FUZZY MAIRCA model for strategy selection in the system of defense”, Discrete Dynamics in Nature and Society, Vol. 2023, pp. 1-14, doi: 10.1155/2023/4961972.
Topaloglu, M., Yarkin, F. and Kaya, T. (2018), “Solid waste collection system selection for smart cities based on a type-2 fuzzy multi-criteria decision technique”, Soft Computing, Vol. 22 No. 15, pp. 4879-4890, doi: 10.1007/s00500-018-3232-8.
UN (2023), The Sustainable Development Goals Report 2023, Special Edition, available at: https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf (accessed 5 November 2023).
UN BRAZIL (2015), “Agenda 2030 para o Desenvolvimento Sustentável”, available at: https://brasil.un.org/sites/default/files/2020-09/agenda2030-pt-br.pdf (accessed 5 November 2023).
Więckowski, J., Król, R. and Wątróbski, J. (2022), “Towards robust results in Multi-Criteria Decision Analysis: ranking reversal free methods case study”, Procedia Computer Science, Vol. 207, pp. 4584-4592, doi: 10.1016/j.procs.2022.09.522.
Zavadskas, E.K., Mardani, A., Turskis, Z., Jusoh, A. and 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, Vol. 15 No. 03, pp. 645-682, doi: 10.1142/S0219622016300019.
Zhang, J.Z., Srivastava, P.R. and Eachempati, P. (2023), “Evaluating the effectiveness of drones in emergency situations: a hybrid multi-criteria approach”, Industrial Management and Data Systems, Vol. 123 No. 1, pp. 302-323, doi: 10.1108/IMDS-01-2021-0064.
Acknowledgements
This work is partially funded by the Brazilian Army – Science and Technology Department – Military Institute of Engineering (Instituto Militar de Engenharia - IME).