Selection of Unmanned Aerial Vehicles by Using Multicriteria Decision-Making for Defence

Abstract

The unmanned systems have been seeing a significant boom in the last ten years in different areas together with technological developments. One of the unmanned systems is unmanned aerial vehicles (UAVs). UAVs are used for reconnaissance and observation in the military areas and play critical role in attack and destroy missions. These vehicles have been winning more features together with developing technology in todays world. In addition, they have been varying with different features. A systematic and efficient approach for the selection of the UAV is necessary to choose a best alternative for the critical tasks under consideration. The multicriteria decision-making (MCDM) approaches that are analytic processes are well suited to deal intricacy in selection of alternative vehicles. This study also proposes an integrated methodology based on the analytic hierarch process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to evaluate UAV alternatives for selection process. Firstly, AHP, a MCDM method, is used to determine the weights of each critical factor. Subsequently, it is utilized with the TOPSIS approach to rank the vehicle alternatives in the decision problem. Result of the study shows that UAV-1 was selected as the most suitable vehicle. In results, it is seen that the weights of the evaluation criteria found by using AHP affect the decision-making process. Finally, the validation and sensitivity analysis of the solution are made and discussed.

Full text

Research Article
Selection of Unmanned Aerial Vehicles by Using Multicriteria
Decision-Making for Defence
Mustafa Hamurcu and Tamer Eren
Department of Industrial Engineering, Kırıkkale University, Kırıkkale, Turkey
Correspondence should be addressed to Tamer Eren; teren@kku.edu.tr
Received 18 March 2020; Revised 27 May 2020; Accepted 28 May 2020; Published 17 June 2020
Academic Editor: Ming-Sheng Liu
Copyright ©2020 Mustafa Hamurcu and Tamer Eren. is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
e unmanned systems have been seeing a significant boom in the last ten years in different areas together with technological developments.
One of the unmanned systems is unmanned aerial vehicles (UAVs). UAVs are used for reconnaissance and observation in the military areas
and play critical role in attack and destroy missions. ese vehicles have been winning more features together with developing technology in
todays world. In addition, they have been varying with different features. A systematic and efficient approach for the selection of the UAV is
necessary to choose a best alternative for the critical tasks under consideration. e multicriteria decision-making (MCDM) approaches that
are analytic processes are well suited to deal intricacy in selection of alternative vehicles. is study also proposes an integrated methodology
based on the analytic hierarch process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to evaluate UAV
alternatives for selection process. Firstly, AHP, a MCDM method, is used to determine the weights of each critical factor. Subsequently, it is
utilized with the TOPSIS approach to rank the vehicle alternatives in the decision problem. Result of the study shows that UAV-1 was
selected as the most suitable vehicle. In results, it is seen that the weights of the evaluation criteria found by using AHP affect the decision-
making process. Finally, the validation and sensitivity analysis of the solution are made and discussed.
1. Introduction
Unmanned aerial vehicles (UAVs) are named such as
remotely piloted vehicles, drones, robot planes, and pi-
lotless aircraft, and in many ways. UAVs can fly auton-
omously or semiautonomously [1]. UAVs became an
important element of many modern militaries and various
civilian areas. Demands have been increasing for a variety
of types of unmanned vehicles due to their success on
battlefields. At the same time, they have capabilities such
as persistent surveillance, tactical and combat recon-
naissance, resilience together with its low benefit risk, and
low cost. Increasing use of these vehicles is rising
alongside increasing demands for battlefield intelligence,
tighter defence budgets of countries, faster operation
tempos, and lowered tolerance for casualties in the de-
fence area. Besides, UAVs are also emerging as a suitable
vehicle for a wide range of civil applications such as di-
saster monitoring and atmospheric observation [2]. As in
developed countries, Turkey is also developing UAVs for
defence and civilian usage.
UAVs had been mainly developed for military appli-
cations and military areas a few years ago. However, now,
they have composed serious enhancements in the design and
capabilities of UAVs with their varying size due to tech-
nological advancements in robotics. Today, UAVs’ nu-
merous civil applications have recently emerged due to their
reliability and operation with a very good level of flight
stability. UAVs have some advantages according to their
alternatives, such as a helicopter. ese advantages are re-
quired to reach places that are difficult to access, long periods
of repetitive work, or operating in dangerous conditions,
especially for extended periods of time or under stressful
conditions and in risky tasks [3, 4]. So, these advantages had
ensured to use these vehicles widely. e architecture of a
normal UAV consists of different five components that are
the flight system, the control system, the monitoring system,
the data processing system, and the landing system.
Hindawi
Journal of Mathematics
Volume 2020, Article ID 4308756, 11 pages
https://doi.org/10.1155/2020/4308756

e selection of the best UAVs with developing technology
is very important in defence and affects the ability and the
necessity of modern armies as well. Being able to perform
reconnaissance and surveillance missions in the most effective
and efficient way with well-chosen UAVs is depended on
decisions of planners and managers. It is very important to
create and invent them, but they are varied with developing
technology day by day. us, a multicriteria selection process is
needed for the best UAV decision with developing technology.
e main objective of this study is to propose a systematic
MCDM model to help in the defence area for the selection of the
most suitable UAV among a set of available alternatives.
Selecting a new UAV is a strategic decision-making process that
has a high complexity because of the criteria, which must be
considered simultaneously. Besides, most of these criteria affect
each other, are contradictory, and an increase in one of the
criteria’s compliance might reduce the compliance of the other.
So, in order to select the best UAV among the alternatives on
the market, we need to consider various evaluation criteria. e
MCDM methods help to choose the best alternative by con-
sidering various criteria and by evaluating all the alternatives.
e academic literature has some examples of the application of
the MCDM in the defence field. Some of them are evaluating
naval tactical missile systems with fuzzy AHP [5]; selection of
attack helicopters by AHP [6]; evaluation of the best main battle
tank with fuzzy decision theory [7, 8]; weapon selection by using
AHP and TOPSIS [9], goal programming [10]; ELECTRE
(elimination and choice translating reality) [11]; evaluating the
military training aircraft by using MCDM under fuzzy logic
[12, 13]; using MCDM methods for determination of the best
military cargo aircraft [14]; selection of investment projects in
the defence industry [15]; and military airport location selection
by using the hybrid application of AHP-PROMETHEE-
(Preference Ranking Organisation Method for Enrichment
Evaluation-) VIKOR (Serbian: Vise Kriterijumska Optimizacija
I Kompromisno Resenje) methods [16]. Although UAVs play
an important role in the design of an effective defence system for
the military area, the academic literature about the selection of
UAVs is limited. e study described in this paper has two
specific objectives: (1) to introduce and describe the importance
of UAV technologies and their applications; (2) to offer an
analytic process that is based on AHP and TOPSIS methods for
the best selection among the alternative UAVs to help decision
makers in the defense area.
e remainder of this study is structured as follows: the
literature review about UAVs is presented in Section 2. In
Section 3, AHP and TOPSIS, multicriteria decision-making
methods, are explained. e selection process by using AHP
and TOPSIS is made among the various UAVs in Section 4.
Conclusions are presented in Section 5.
2. UAVs in the Literature
Although UAVs are developed for military purposes, in-
cluding reconnaissance and attack roles, they are quickly
gaining popularity worldwide in various civil areas. However,
now, they are used in lots of areas. UAV-based systems have
many advantages according to manned air vehicles: first, the
cost of use related to the low purchase, management, and
operation costs. ey can yield high-resolution images which
are useful for traffic analysis based on video image processing.
UAVs have some disadvantages such as low battery duration,
battery life, limited UAV payload, vegetation, buildings, ur-
ban canyons, limited take-off mass, limited wing surface,
limited wing loading, and no-fly zones which affect negatively.
So, their applications for this field are limited and influenced
by some factors affecting their performance, such as weather
conditions should be mentioned, technical instrumental
problems, and physical obstacles [17].
UAVs are widely used, especially in traffic and the
construction industry. UAVs have been used for various
aims such as safety/security monitoring, inspections, sur-
veying, and aerial photography in the construction industry
[18]. In traffic, there are applications such as to obtain
detailed traffic information on real time [19]; to evaluate
traffic flow conditions in urban areas using videos through
the UAV [20]; and to estimate traffic flow parameters of a
road intersection through a video image processing tech-
nique using an UAV [21]. Lee et al. mentioned about traffic
and roadway incident monitoring via the UAV [22]. Khan
et al. presented an extensive systematic and practical study
on how to conduct an UAV-based traffic study [23]. Salvo
et al. developed a new methodology to evaluate the real
traffic flow conditions. In their methodology, they used the
videos acquired by UAVs [20]. Barmpounakis et al. aimed to
review research dedicated to using unmanned aerial systems
in transportation in their study. ey mentioned the ad-
vantages of the airborne video as a means for acquiring high-
quality naturalistic data for both practitioners and re-
searchers in their study [24].
Besides, these UAV applications are applied in different
areas such as inspection of critical linear infrastructure such
as oil and gas pipelines or electrical transmission lines and
inspection of wind turbine blades by UAVs with imaging.
UAVs are also used in the real-estate industry on the
purpose of conducting aerial surveys and mapping of
planned developments or to document transactions. At the
same time, UAVs are being used in decision-making in
agriculture on whether crops need to be watered and where
to apply the fertilizer. Also, G¨
ul [25] mapping operations in
open-pit mines are made by using the UAV Erdelj and
Natalizio search review of main disaster management ap-
plications with UAV [26].
ere are some papers about UAVs and their various
applications in the literature. Hassanalian and Abdelkefi
gave some brief information related to the civil uses of UAV/
drones and their fields of use. Also, they provided com-
prehensive information on the current status of the legal
frames and regulations in Turkey and in the world [27].
Akg¨
ul et al. evaluated UAV and its systems. At the same
time, they evaluated the usage of these vehicles for the
forestry area [28]. Wu et al. discussed the development of a
multiobjective mission flight planning algorithm for un-
manned aerial system (UAS) operations within the National
Airspace System (NAS) in their study [29]. Wu et al. pre-
sented a system for automated mission planning with a view
to operate UAVs. Decision variables for their system were
fuel consumption, flight time, wind and weather conditions,
2Journal of Mathematics

terrain elevation, airspace classification, and the flight tra-
jectories of other aircrafts [30]. Arıca et al. presented a
multicriteria path planning model for UAVs. eir model
helps in the planning of optimal paths in terms of time,
distance, and fuel consumption [31]. Caner demonstrated
the pros and cons of UAVs compared with manned aircraft
in his study. Besides, he provided information about the
historical developments that have occurred and new tech-
nologies in the UAV [32].
Kiracı and Bakır focused on the selection of the aircraft
to determine the most suitable aircraft for airline com-
panies with different flight networks and different flight
destinations with the TOPSIS method [33]. Ercan and
Gencer investigated the literature on “dynamic route
planning” for unmanned aerial systems [34]. Lin and Hung
selected military UAVs using the fuzzy weighted average
algorithm. ey used three main criteria in their study that
are mission flexibility, operational suitability, and opera-
tional assessment [35].
Besides, some researchers, as distinct from the selection
of UAVs, studied about aircraft type selection with mul-
ticriteria decision-making methods [36–38]. See et al.
presented a multiattribute methodology for selecting the
best aircraft among a set of alternatives. e authors used
the method of hypothetical equivalents and inequivalents.
ey used three criteria: speed, range, and the number of
passengers [39]. Yeh and Chang proposed a fuzzy multi-
criteria decision-making algorithm with group decision-
making for evaluation of the performance of each aircraft.
eir study that the performance of each aircraft is eval-
uated through fuzzy rating used three main criteria and
eleven subcriteria (main criteria: technological advances,
social responsibility, and economic efficiency; subcriteria:
aircraft maintenance capability, pilot adaptability, aircraft
reliability, maximum range, passengers’ preference, the
level of noise, operational productivity, airline fleet
economy of scale, operating cost, purchasing price, and
corporate strategy) [40]. Gomes et al. proposed a fuzzy
stochastic approach for the selection of aircraft. NAIADE
method (Novel Approach to Imprecise Assessment and
Decision Environments) was used in their evaluation
process based on three criteria (financial, logistics, and
quality), further articulated to twelve subcriteria (acqui-
sition cost, liquidity, operating costs, range, flexibility,
cruising speed, replacement parts availability, landing and
take-off distance, comfort, avionics, availability, and safety)
[41]. Bruno et al. proposed a model aircraft evaluation in
their study. e model proposed includes four main criteria
(economic performance, technical performance, aircraft
interior quality, and environmental impact) and eight
subcriteria such as aircraft price, operative cost, cruise
speed, autonomy, seat comfort, cabin luggage compart-
ment size, noise, and environmental pollution. eir aim is
to propose a novel model for aircraft evaluation according
to the airlines’ needs [42]. Doˇ
zi´
cet al. proposed a new
methodology for the aircraft type selection problem with
regard to different criteria that involve quantitative and
qualitative aspects, three main criteria (aircraft charac-
teristic, cost, and added value indicators), and ten
subcriteria. ey used opinions of experts from different
airlines and universities for the analytic evaluation process.
Fuzzy AHP was used due to the uncertainty of decision
problems in their study [43]. Petkovics et al. studied dif-
ferent subjects from other academics. ey selected the
appropriate drone for the specific needs of farmers to
collect the necessary data for precision agriculture using
drones. ey selected the best drone among the two drone
types for data collection in agriculture [44].
In this context, this paper proposes a novel model for
UAV selection, which aims to overcome the complexity of
the UAVs’ evaluation process. e integrated AHP-TOPSIS
methods that are multicriteria decision-making methods are
used for the selection process. ese methods include a
simple analytic process and basic calculations. e hybrid
model proposed has a lower level of computational com-
plexity, which facilitates its practical application. So, a
systematic decision-making process was developed via the
usability of the model and its applicability that helps decision
makers in the UAV selection process.
e contribution of the paper could be perceived
through two main issues: the first one is related to the se-
lection criteria used as the model input, which is of crucial
importance for decision-making and modeling, while the
second one refers to the MCDM technique. Based on the
analysis of the relevant literature, the criteria used in the
decision-making process of aircraft type selection are
identified. Also, the contribution of the paper is the pro-
posed methodology for UAV selection among the aircraft
selection problem based on the integrated MCDM methods
for the first time. We see various studies of aircraft selection
using MCDM methods done in our literature research. And
we see a few studies such as planning of the UAV route and
its mission planning. However, literature is limited to the
UAV selection process using MCDM methods. Besides,
numerous articles have recently been published about
various UAV applications in military use and research, have
been a major driver for advancing UAV technology, and
have made a big contribution. However, academic studies on
decision-making about UAVs are limited. is study will
contribute to the literature in this respect.
3. Multicriteria Decision-Making
In this section, the AHP and TOPSIS methods are pre-
sented. is study utilizes two MCDM methods, AHP to
determine the weights of criteria and TOPSIS to rank
alternatives and select the best alternative UAV. A brief
description and steps of each method are, respectively,
provided as follows.
3.1. Analytic Hierarchy Process. Analytic hierarchy process
(AHP) is a flexible and effective decision-making process,
developed by omas L. Saaty. is method is useful,
making the best decision for quantitative and qualitative
aspects of a decision in establishing priorities. e AHP
method has been applied in various fields: management,
production, transportation, agriculture, industry, allocation,
Journal of Mathematics 3

and distribution of resources in the complex decision
problems solving strategic decisions. ere are some causes
of common usage of the AHP: (1) helps decision makers to
find important degree in making simultaneous evaluation in
the decision-making problem; (2) includes relatively basic
mathematical calculation as compared to other analytic
methods; (3) is flexible to be integrated in various decision-
making methods such as ranking and programming models;
and (4) has the ability to control mathematical calculations
and judgment of decision makers [45]. A decision hierarchy
structure of AHP includes three levels that are the goal, the
criteria, subcriteria, and the alternatives. e hierarchy
structure makes the problem more understandable and
clearer for the decision makers at the decision process
[46, 47]. e selection process or calculating the weights in
AHP has five major steps [48, 49]:
Step 1: determining decision problems, alternatives,
and criteria. Establishing a matrix comparing the cri-
teria and alternative pair wisely by using Saaty’s scale.
Saaty’s scale: Extreme Importance-9; Very Strong
Importance-7; Strong Importance-5; Moderate Im-
portance-3; and Equal Importance-1.
Step 2: calculate the criteria weights with pairwise
comparisons. So, find relative importance of weights of
evaluation criteria in the hierarchy by using the scale,
1–9 points, of Saaty. And pairwise comparison matrices
are created.
For example, n×ncomparison matrix ais created for n
criteria to the relative importance of the criterion iand
the criterion j. Among the amatrix elements is the
following connection:
aij �1,
aij �1
aij
.
(1)
Step 3: then, the normalized decision matrix is created
Step 4: calculate the consistency index (CI) measured as
follows:
Firstly, calculate the
ʎ
max
value for total consistency;
CI �%ʎmax −n
n−1.(2)
Step 5: calculate a consistency ratio (CR). If the CR is
less than 0.10 (CR <0.1), then the ratio shows an ac-
ceptable level of consistency in the AHP. If CR is more
than 0.10 (CR>0.1), the ratio is inconsistent as follows:
CR �CI
RI <0.10,(3)
with random index (RI) as given in Table 1.
3.2. TOPSIS. e technique for order preference by
similarity to ideal solution (TOPSIS) technique was
established by Hwang and Yoon, an approach which
presumes that each criterion tends toward a monotoni-
cally decreasing or increasing utility [50, 51]. e ne-
cessitation of having the shortest distance to the positive
ideal solution/the farthest distance from the negative
ideal solution for the selection of alternatives is the
fundamental concept of this technique [52]. It suggests
the Euclidean distance strategy for this process that as-
sesses the relative closeness of the selected alternatives to
the ideal solution. us, a series of comparisons of these
relative distances can be obtained with the preference
order of the alternatives [53].
e first step includes the creation of an evaluation
matrix which consists of malternatives and ncriteria. e
intersection of each alternative with each criterion is given
as x
ij
, and therefore, the matrix can be described as
(x
ij
)
m×n
. e second step includes the normalization of the
matrix:
R�rij
􏼐 􏼑m×n,where rij �xij
�������
􏽐m
i�1x2
ij
􏽱
⎛
⎜
⎜
⎜
⎝⎞
⎟
⎟
⎟
⎠,
I�1,2,. . . , m;j�1,2,..., n.
(4)
e third step includes the calculation of the weighted
normalized decision matrix:
tij �rij ∗wj,1, I �1,2,..., m;j�1,2,. . . , n, (5)
where
wj�Wj
􏽐n
j�1Wj
, j �1,2,..., n, (6)
so that 􏽐n
j�1Wj�1, and wjis the original weight given to
the indicator vj,j�1, 2, ...,n. On the other step, we calculate
the worst alternative (A−) and the best alternative (A+).
In the decision process, equation (7) is used to determine
the distance between each alternative and the positive ideal
point. e distance between each alternative and the neg-
ative ideal point can be determined with equation (8) uti-
lizing the same separation measure [54]:
d∗
ij ��������������
􏽘
n
i�1
Vij −V∗
i
􏼐 􏼑2
􏽶
􏽴,(7)
d−
ij �������������
􏽘
n
i�1
Vij −V−
i
􏼐 􏼑2
􏽶
􏽴, j �1,2,....(8)
Table 1: Random index (RI).
No. of
criteria 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
4Journal of Mathematics

e relative closeness (CC ∗
j) to the positive ideal solu-
tion can be calculated by the following equation
CC∗
j�d−
j
d−
j+d+
j
, j �1,2,. . . ,(9)
where the CC∗
jindex value lies between 0 and 1. e larger
the index value means the better the performance of the
alternative. e TOPSIS technique usually deals with benefit
and cost data. In this paper, the positive ideal solution (PIS)
is the one with the lowest cost and most benefits of all al-
ternatives, and the negative ideal solution (NIS) is the one
with the highest cost and lowest benefits of all alternatives.
AHP and TOPSIS are used widely for decision process
such as route selection [55], technology selection [56],
project selection [57], location selection [58], and vehicle
selection [59]. Besides, these methods have been used in
studies like maintenance strategy selection [60], supplier
selection [61], evaluation of low-emission energy technol-
ogies [62], optimization of maintenance strategies [63, 64],
electric bus selection [65], strategic planning [66], and
supplier selection [67].
4. Selection of the Unmanned Aerial Vehicles
e proposed decision model, composed of AHP and
TOPSIS methods, consists of three stages. Firstly, identifying
the criteria to be used in the model; second, finding the
weight of criteria using AHP computations; and third, the
final ranking of UAVs with TOPSIS.
To fulfill this selection process, the AHP-TOPSIS hybrid
model has been selected in this research due to three causes.
ese three causes are simple mathematical and computa-
tional process, ranking the alternative locations based on
their overall performance, and finally, the information re-
quirements of the proposed framework are stratified into a
hierarchy to simplify the information input and allow a
selection problem to focus on a small area of the large
problem. Besides, inconsistencies of the experts can be
measured with CR values in these decision processes. A
three-step methodology has been used in this study to the
UAV selection process (see Figure 1).
4.1. Identification of the Criteria and Expert Team. UAVs
have experienced major development and gained fast-
growing popularity worldwide during the last several de-
cades. Nowadays, these vehicles are extensively used in
various critical military and defence applications for re-
connaissance, surveillance, and security reinforcement [68].
Evaluation of UAVs plays an important role in the design of
an effective defence system. e producers of UAV are
constantly innovating and improving their UAVs. ese
developing processes aim to answer to both needs of
themselves and their potential customers. Besides, these
processes are improved of technical features that deal with
flight parameters such as flying range, speed, load capacity,
and autonomous. In this scope, we evaluated six alternative
UAVs for the defence area. We use seven performance
criteria that are important criteria which belong to UAVs.
ese criteria are wingspan (C1), length (C2), payload ca-
pacity (C3), endurance (C4), cruise speed (C5), operational
altitude (C6), and range (C7). ese criteria are important
for UAVs. For example, the criterion of the payload is one of
the most important criteria for UAVs. With its capable
payloads onboard, UAV can not only detect a target but also
able to mark it with its laser designator and can attack. UAVs
can take off, land, and fly fully autonomously for a mission
without assistance from a pilot fully autonomously without
assistance from a pilot. e payload capacity and variations
are available for both civil and military applications. Criteria
to be considered in evaluating UAVs are determined by the
expert team. Furthermore, the judgments of experts are used
in this methodology to evaluate the UAV selection process.
e utilization of expert opinions is proposed in this
methodology to define the problem statement, to identify the
criteria for evaluating UAVs, and to the weight of the criteria
in decision-making. e expert team determined six possible
Identify and select experts for
decision problem
Determining alternative UAVs
Determining the criteria using
expert opinion
Decision hierarchy
Calculation of evaluation criteria
using AHP
Ranking of UAVs using TOPSIS
Selecting the optimal UAV using
TOPSIS
Validation using VIKOR
Preparation step
Approve
decision
hierarchy?
Approve
criteria
weights?
Evaluation step
Yes
Yes
No
No
Weight
calculation step
Analysis of the solution using
Spearman’s rank correlation test
Figure 1: e schematic diagram for methodology.
Journal of Mathematics 5

UAVs suitable for the needs for defence. e qualifications
of experts are academic title, experience, and working area or
institution, respectively: Expert 1, Prof Dr., 24, Optimiza-
tion, Scheduling, and Decision-making/Expert 2, Assistant
Prof, 12, Optimization and Energy/Expert 3, Assistant Prof,
10, Optimization/Expert 4, Assistant Prof, 12, Optimization/
Expert 5, Research Assistant, 4, Decision-making/Expert 6,
Mechanical engineer, 22, Turkish land forces command/
Expert 7, Mechanical engineer, 23, Turkish land forces
command. UAVs and their specifications are given in
Table 2.
is study aims selection of the best UAV as in the goal
shown in the hierarchy’s top. Six alternatives and seven
criteria are shown in the hierarchy structure in the same
figure.
In AHP, the pairwise comparison matrix (Table 3) is
formed to determine the criteria weights. Academic experts
make evaluations using Saaty’s 1–9 scale to determine the
values of the criteria of pairwise comparison matrices. Ta-
ble 4 shows the weight of criteria that are found as the result
of comparisons with AHP. e criteria weights in the
descending order are shown in Table 4 as can be seen that C3
and C5 were rated more important than the remaining
criteria. Besides, C4 and C7 were rated relatively lower, and
C1, C2, and C6 became the lowest-rated criteria. e
consistency ratio is found by using formulas (1) and (2) and
random index (RI). is value is an acceptable level. e
decision hierarchy for selection of the best UAVs is shown in
Figure 2.
It is to obtain a weighted decision table in the TOPSIS
method using the criteria weights calculated by AHP. e
resulting weighted decision matrix in TOPSIS process is
shown in Table 5. Positive ideal and negative ideal solution
values are shown in Table 6. In this problem, C1 and C2 are
cost criteria, whereas the other criteria, C3, C4, C5, C6, and
C7, are benefit criteria.
Finally, the experts evaluated the six UAV alternatives
for each evaluation criterion with AHP. e weight of the
criteria has been calculated to ensure of the TOPSIS
weighted valuation matrix for the UAVs. e AHP-TOPSIS
methodology, the decision matrix described, is made in
Table 2. In the following process, the decision matrix is
normalized and is shown in Table 4, together with weighted
values. Table 5 shows the calculated weighted normalized
matrix. In the following step, the ideal and negative ideal
solutions are determined using equations (3) and (4), shown
in Table 6. en, with equation (5), the distance from PIS
and NIS is calculated, respectively, as their results are shown
in Table 7. Eventually, the ranking has been calculated, and
the score of each alternative is shown in Table 7.
Consequently, the distance of each vehicle from A+ to
A−can be currently calculated. e last step solves the
similarities to an ideal vehicle. Based on CCi values in Ta-
ble 7, the ranking of the UAVs in order is UAV-5, UAV-6,
UAV-1, UAV-2, UAV-3, and UAV-4. AHP-TOPSIS model
results indicate that UAV-4 is the best vehicle with the CCi
value of 0.6173.
Two different decision processes are applied as the
TOPSIS method and the AHP-weighted TOPSIS method.
e CCi values obtained in this condition are presented in
Table 7, with their comparisons with previous values. Based
on unweighted CCi values, the ranking of the UAV systems
in order is UAV-1, UAV-5, UAV-2, UAV-6, UAV-4, and
UAV-3. e results changed according to the unweighted
ranking. e rankings of the considered alternatives as
derived by employing these two methods are exhibited in
Figure 3.
4.2. Validation and Sensitivity Analysis of the Solution.
TOPSIS and VIKOR (Vise Kriterijumska Optimizacija I
Kompromisno Resenje) methods are distance-based
similar methods. ere is a difference between the deci-
sion process. While the VIKOR method uses linear
normalization, TOPSIS uses vector normalization pro-
cedure. However, two methods have simple mathematical
calculations and have been widely used for the complex
decision process. In this section, we use the VIKOR
method to validate TOPSIS results. Besides, Spearman’s
rank correlation test is used for control of the statistical
similarities among the results of ranking methods. See, for
a more detailed description of the VIKOR method and its
decision-making process, [69–72].
e decision problem is solved separately with the
VIKOR method, and AHP-weighted VIKOR solution results
are given comparatively in Table 8. e results of all methods
are also shown in Figure 4 graphically.
e scores of criteria obtained by the AHP method are
also used for two rankings. e rating scores of the TOPSIS
applications are then compared with the ones obtained using
the VIKOR method results by using Spearman’s rank cor-
relation test and proposed in Table 9. As the outcome of
Spearman’s rank correlation test, the results are provided in
the same table.
Table 2: UAVs and their specifications.
Specif. Unit Alternative UAVs
UAV-1 UAV-2 UAV-3 UAV-4 UAV-5 UAV-6
C1 mm 3100 3400 5110 2200 6500 10500
C2 mm 1200 3110 4200 1000 4500 6500
C3 kg 6 50 5 1.5 50 70
C4 hour 20 6 5 3 12 20
C5 km/h 90 180 150 80 110 80
C6 km/h 5000 4900 450 4500 5500 6800
C7 km/h 1800 600 600 150 1300 150
Table 3: Pairwise comparison matrix.
Crt. C1 C2 C3 C4 C5 C6 C7
C1 1.000 3.000 0.333 0.333 0.333 0.200 0.333
C2 0.333 1.000 0.333 0.333 0.333 0.200 0.200
C3 3.000 3.000 1.000 1.000 3.000 3.000 0.333
C4 3.000 3.000 1.000 1.000 3.000 1.000 0.333
C5 3.000 3.000 0.333 0.333 1.000 1.000 0.333
C6 5.000 5.000 0.333 1.000 1.000 1.000 0.333
C7 3.000 5.000 3.000 3.000 3.000 3.000 1.000
6Journal of Mathematics

In our study, the critical Zvalue is 1.645 which is selected
at the level of significance of α�0.05. Each Zvalue (TOPSIS,
AHP-TOPSIS, VIKOR, and AHP-VIKOR) is higher than
1.645. It can be stated that the ranking provided by VIKOR
applications is statistically similar to the other TOPSIS
applications. In conclusion, the most suitable UAV is se-
lected by using AHP-TOPSIS. VIKOR and its applications
support each result. However, the results show that there are
small differences in ranking of methods.
5. Conclusion
e UAV selection is very important in decision-making
process in terms of the success of the defense area. e
objective of this paper is also to present an integrated
MCDM approach for determination of the best UAV.
erefore, AHP and TOPSIS methods are used together.
e UAVs are an increasingly important element of
many modern militaries and various civilian areas. So, there
is a need for selection process for the UAVs among various
technologies. erefore, there are a lot of criteria affecting
Selection of the best UAV
C1 C2 C3 C4 C5 C6 C7
UAV-1 UAV-2
GoalCriteriaAlternatives
UAV-3 UAV-4 UAV-5 UAV-6
Figure 2: e decision hierarchy for the UAV selection process.
Table 7: Final ranking for only TOPSIS and AHP-TOPSIS.
Alternatives
Only the TOPSIS
method
AHP-TOPSIS
method
CCi Ranking CCi Ranking
UAV-1 0.6257 1 0.6623 2
UAV-2 0.5578 3 0.4216 4
UAV-3 0.3198 6 0.2374 5
UAV-4 0.4283 5 0.1944 6
UAV-5 0.5829 2 0.6747 1
UAV-6 0.4755 4 0.4273 3
Table 4: Criteria and their important score.
Crt. C1 C2 C3 C4 C5 C6 C7 Imp. score
C1 0.0545 0.1304 0.0526 0.0476 0.0286 0.0213 0.1163 0.0645
C2 0.0182 0.0435 0.0526 0.0476 0.0286 0.0213 0.0698 0.0402
C3 0.1636 0.1304 0.1579 0.1429 0.2571 0.3191 0.1163 0.1839
C4 0.1636 0.1304 0.1579 0.1429 0.2571 0.1064 0.1163 0.1535
C5 0.1636 0.1304 0.0526 0.0476 0.0857 0.1064 0.1163 0.1004
C6 0.2727 0.2174 0.0526 0.1429 0.0857 0.1064 0.1163 0.1420
C7 0.0545 0.1304 0.0526 0.0476 0.0286 0.0213 0.1163 0.3155
Consistency ratio (CR) 0.08388 <0.10
Table 5: Weighted evaluation matrix for the UAVs.
Alt. C1 C2 C3 C4 C5 C6 C7
Weights 0.0645 0.0402 0.1839 0.1535 0.1004 0.1420 0.3155
UAV-1 0.014 0.005 0.011 0.096 0.030 0.059 0.238
UAV-2 0.015 0.013 0.092 0.029 0.061 0.058 0.079
UAV-3 0.023 0.018 0.009 0.024 0.051 0.005 0.079
UAV-4 0.010 0.004 0.003 0.014 0.027 0.053 0.020
UAV-5 0.029 0.019 0.092 0.058 0.037 0.065 0.172
UAV-6 0.047 0.027 0.129 0.096 0.027 0.080 0.020
Table 6: Determination of positive ideal and negative ideal
solutions.
A+ A−
0.010 0.047
0.004 0.027
0.129 0.003
0.096 0.014
0.061 0.027
0.080 0.005
0.238 0.020
Journal of Mathematics 7

this decision process. All these criteria should be evaluated
with various dimensions. Six alternatives under the seven
criteria were evaluated, and the model solution was
established with multicriteria decision-making in this
study. Finally, the best UAVs among the alternatives were
ranked.
5.1. Importance of is Study. Importance of this study is to
help developing countries in the defence area for their
decisions to select among the UAV alternatives. UAVs are
project-based applications and are strategical vehicles for
national security. So, this study will be a good guide which
helps developing countries.
5.2. Recommendations for Future Studies. In future studies,
the other MCDM methods such as analytic network process
(ANP), ANP-TOPSIS, or fuzzy methods can be used, and the
obtained results can be compared. e optimal solution of
UAV selection can be done by mathematical models like
goal programming or integer programming under resource
constraints such as budget. It can be used as the zero-one
goal programming model, by which the AHP/ANP priority
0
1
2
3
4
5
6
7
UAV-1 UAV-2 UAV-3 UAV-4 UAV-5 UAV-6
TOPSIS
AHP-TOPSIS
Figure 3: Comparison of TOPSIS and AHP-TOPSIS.
Table 8: Comparison of company ranking approaches.
UAVs
Unweighted methods AHP weighted
TOPSIS (A) VIKOR (B) AHP-TOPSIS (C) AHP-VIKOR (D)
CCi Rank Pi Rank CCi Rank Pi Rank
UAV-1 0.6257 1 0.3905 3 0.6623 2 0.1733 2
UAV-2 0.5578 3 0.2865 2 0.4216 4 0.4723 3
UAV-3 0.3198 6 1.0000 6 0.2374 5 0.7565 5
UAV-4 0.4283 5 0.9152 5 0.1944 6 1.0000 6
UAV-5 0.5829 2 0.1705 1 0.6747 1 0.0699 1
UAV-6 0.4755 4 0.8436 4 0.4273 3 0.7123 4
0
2
4
6
8
UAV-1 UAV-2 UAV-3 UAV-4 UAV-5 UAV-6
Rank
Alternatives
A
B
C
Figure 4: Comparison of ranking approaches.
Table 9: Spearman’s rank correlation test.
Alternatives Ranking differences
A-B A-C A-D B-C B-D
UAV-1 −2−1−1 1 1
UAV-2 1 −1 0 −2−1
UAV-3 0 1 1 1 1
UAV-4 0 −1−1−1−1
UAV-5 1 1 1 0 0
UAV-6 0 1 0 1 0
Spearman’s rank coefficient
(rs) 0.829 0.829 0.886 0.771 0.886
Statistical significance value
(Z)1.853 1.853 1.981 1.725 1.981
8Journal of Mathematics

weights can be combined with the objective function. Be-
sides, one can focus on selection of specific criteria that are
key performance drivers that can lead to informed selection
of the UAV for successful decision-making. e criteria used
in the proposed model can be improved with additional
criteria.
e proposed model can be also used in the other im-
portant decision processes such as drone selection and attack
helicopter selection. e model can also be used with minor
modifications in other decision-making processes in the
defence area. In addition, mathematical models such as goal
programming can be combined with this model. Besides, the
weapon and the weapon system selection for UAVs can be
done with MCDM. e qualitative criteria together with the
quantitative criteria such as human factor, flying, and
handling qualities can be added to the proposed model.
At the same time, UAV selection process with MCDM
can be used in traffic control and surveillance, infrastructure
inspection, maintenance, security, precision agriculture, and
also smart cities. Especially, the unmanned vehicle selection
for traffic control and surveillance is a very important point
for megacities.
Data Availability
e data used to support the findings of this study are
available from the corresponding author upon request.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
Authors’ Contributions
Mustafa Hamurcu contributed to formal analysis, funding
acquisition, investigation, methodology, project adminis-
tration, and resources. Tamer Eren supervised and validated
the study. Mustafa Hamurcu and Tamer Eren contributed to
writing, reviewing, and editing of the paper.
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