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Recent Advances in Electrical & Electronic Engineering
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eISSN: 2352-0973
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Recent Advances in Electrical & Electronic Engineering, 2021, 14, 779-801
779
SYSTEMATIC REVIEW ARTICLE
Multi-criteria Decision Making: A Systematic Review
Nayli Adriana Azhar1, Nurul Asyikin Mohamed Radzi1,2,* and Wan Siti Halimatul Munirah Wan
Ahmad1
1Electrical and Electronics Engineering Department, College of Engineering, Universiti Tenaga Nasional, Kajang
43000, Malaysia; 2The Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000,
Malaysia
Abstract: Multi Criteria Decision Making (MCDM) helps decision makers (DMs) solve highly
complex problems. Accordingly, MCDM has been widely used by DMs from various fields as an
effective and reliable tool for solving various problems, such as in site and supplier selection, rank-
ing and assessment. This work presents an in-depth survey of past and recent MCDM techniques
cited in the literature. These techniques are mainly categorised into pairwise comparison, outranking
and distance-based approaches. Some well-known MCDM methods include the Analytical Hierar-
chy Process (AHP), Analytical Network Process (ANP), Elimination et Choix Traduisant la Realité
(ELECTRE), Preference Ranking Organization METHod for Enrichment of Evaluations (PROME-
THEE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKrite-
rijumska Optimizacija I Kompromisno Resenje (VIKOR). Each of these methods is unique and has
been used in a vast field of interest to support DMs in solving complex problems. For a complete
survey, discussions related to previous issues and challenges and the current implementation of
MCDM are also presented.
A R T I C L E H I S T O R Y
Received: September 2, 2021
Revised: October 6, 2021
Accepted: October 10, 2021
DOI:
10.2174/2352096514666211029112443
Keywords: multi-criteria decision making, industrial decision making, analytical hierarchy process, analytical network process,
electre, vikor, topsis, promethee.
1. INTRODUCTION
In many fields, decision makers (DMs) need to address a
problem systematically, accurately and reliably according to
their preferences. Multi-Criteria Decision Making (MCDM)
is particularly useful in this area, given that each problem
requires a unique decision. MCDM deals with complex prob-
lems depending on the solution methods selected by DMs.
Specifically, MCDM breaks down a problem into small
pieces to help DMs clearly view the issue at hand [1]. Given
these benefits, MCDM has been widely used in many fields,
such as management, economics, medicine, environment and
energy. MCDM is mainly used to improve the quality, effi-
ciency, rationality and explicitness of decisions. One main
field that implements MCDM is renewable energy resources
(RES). The authors in a study [2] used various MCDM ap-
proaches to rank the priority of RES and to recommend a
suitable RES to be implemented in Taiwan. Similarly, the
authors in [3] proved MCDM as a reliable and effective poli-
cy making support tool for achieving the most suitable solu-
tion. Integrating MCDM may also increase the chances for
*Address correspondence to this author at the The Institute of Informatics
and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000,
Malaysia; Tel/Fax: 03-89212020 Ext: 2350/3286;
E-mail: asyikin@uniten.edu.my
DMs to achieve solid outcomes. The recent advancement of
MCDM in RES has been mainly ascribed to the increasing
investments in RES as a solution to the depleting global fos-
sil fuel supply.
MCDM can also be used in supply selection domains in
production, manufacturing and other related fields. Industrial
practitioners view this approach as a crucial step in produc-
ing high-quality products and advancing towards sustainable
development. For instance, the proposed model in a study [4]
integrates several MCDM methods to help achieve a green
supplier selection. The proposed model was proven to pro-
vide valuable support for solving complex problems. Similar
approaches were applied in another study [5], which mainly
focused on an easily implementable green supply chain man-
agement. In transportation, MCDM was used as an efficient
indicator of passenger’s satisfaction in public transport in
Tehran [6], where the authors proposed a novel traffic policy
for reducing the number of private cars traveling on the
streets during rush hour, hence avoiding traffic congestion
and encouraging people to use public transportation. In an-
other study [10], the authors used MCDM to evaluate the
performance of rapid bus transit systems in a bid to promote
the satisfaction of customers with this transportation mode.
MCDM approaches can be divided into two subcatego-
ries [8, 11], namely, Multi-Attribute Decision Making
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780 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
Table 1. Comparison between previous MCDM review papers and the proposed review paper.
Author Paper Description Proposed Paper Description
Abbas et al. [1]
Review of the literature published between 2000 and 2014
Includes some basic overview of the selected method
The authors classify each utilized method and the paper is
reviewed generally
Presents a comprehensive review of the literature across various fields,
including renewable energy, sustainability, manufacturing, production and
transportation
Reviews papers published between 2008 and 2021
Covers the processes, advantages and disadvantages of each method
Reviews six widely used methods and briefly mentions others
Each reviewed paper is summarized and explained in detail
Summarizes each application in table form and classifies them based on the
method
Mentions several software widely used
Highlights the advantages and disadvantages of each reviewed method
The issues and challenges encountered in implementing MCDM are dis-
cussed
Velasquez and Hester
[7]
Reviews papers published between and 2002 and 2013
Provides a brief explanation for each method
Literature review on 12 methods
Focuses only on the methods and does not critically review
their application
Highlights the advantages and disadvantages of each re-
viewed method
Singh and Malik [8]
Reviews the literature focusing on MCDM only
Covers only three major MCDM methods
Each reviewed method is illustrated in detail (as numerical
equations) and described step by step
Presents some calculation examples
Kumar et al. [9]
Presents a comprehensive review of the literature on sus-
tainable renewable energy development
Reviews papers published until 2016
Highlights the steps, strengths and weaknesses of each
method
Presents the software mainly used when applying MCDM
Summarises each application in table form
Classifies each method based on region
Penadés-Plà [11]
Presents a comprehensive review of the literature on sus-
tainable bridge design
Reviews papers published until 2016
Presents the tools that can be integrated with MCDM
Address the drawbacks of classic MCDM techniques
Summarises each MCDM method in term of bridge design
criteria
(MADM) and Multi-Objective Decision Making (MODM).
In MADM, DMs select, classify, rank or prioritise a finite
number of alternatives and then obtain the best solution.
MADM has three main approaches, namely, pairwise com-
parison, outranking and distance-based approaches. Pairwise
comparison mainly involves assessing and comparing the
importance of several criteria based on a fundamental scale.
Pairwise comparisons often utilise Analytical Hierarchy Pro-
cess (AHP) and Analytical Network Process (ANP). Mean-
while, outranking approaches provide several alternatives
and check whether one alternative has any degree of domi-
nance over another [12]. These approaches are particularly
suitable in cases with unclear and incomplete information
[11]. Some example outranking approaches include the
ELimination Et Choix Traduisant la REalité (ELECTRE)
and Preference Ranking Organization METHod for Enrich-
ment of Evaluations (PROMETHEE) [13]. Distance-based
approaches evaluate how far away a solution is from the ide-
al point, and the solution with the shortest distance to this
point is considered optimal. Some well-known distance-
based approaches include the Technique for Order of Prefer-
ence by Similarity to Ideal Solution (TOPSIS) [14] and
VIseKriterijumska Optimizacija I Kompromisno Resenje
(VIKOR). Studies aiming to promote maintenance delivery
mainly utilise MADM given the availability of a finite num-
ber of solutions in maintenance management [15]. Mean-
while, MODM deals with situations involving multiple DMs
and an infinite number of choices. In this approach, experts
only take part at the end of the process and choose among
several solutions. Some well-known MODM methods in-
clude Goal Programming (GP) [16, 17] and Genetic Algo-
rithm (GA) [18, 19].
Instead of viewing these approaches individually, this pa-
per focuses on MCDM as a whole. Table 1 compares several
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 781
Fig. (1). Overall view of MCDM approaches. This paper will be focusing on the shaded categories.
Fig. (2). Flowchart of the PRISMA study selection process.
Table 2. Common terms used in MCDM.
Term Definition
The decision-maker (DM) Where experts are assigned to weigh tasks given
Goal What DM wants to achieve
Criteria or attributes The alternatives to be compared with and evaluated
Alternatives The methods or decisions that are to be chosen or ranked
Weights Representing the relative importance of the criteria
MCDM
MODM
MADM
Pairwise
Comparison
Outranking
Distance-
based
AHP
ANP
ELECTRE
PROMETHEE
TOPSIS
VIKOR
782 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
Table 3. Definitions of acronyms and notations.
Acronym Definition
AHP Analytical Hierarchy Process
ANP Analytical Network Process
BWM Best-Worst Method
COPRAS Complex Proportional Assessment
DEMATEL Decision Making Trial and Evaluation Laboratory
DANP DEMATEL-ANP
DEA Data Envelopment Analysis
DG Distributed Generator
DGs Distributed Generations
DM Decision-Maker
DMs Decision Makers
ELECTRE ELimination Et Choix Traduisant la REalité
EVS Electric Vehicle Sharing
FAHP Fuzzy AHP
GA Genetic Algorithm
GCPVS Grid-Connected Photovoltaic System
GIS Geographical Information Systems
GM Green Manufacturing
GMP Green Manufacturing Practices
GP Goal Programming
GRA Grey Relational Analysis
HFLTS Hesitant Fuzzy Linguistic Term Set
IDM Islanding Detection Method
IGRA Improved Grey Relational Analysis
LCRPS Large Commercial Rooftop Photovoltaic System
MADM Multi-Attribute Decision Making
MAUT Multi-Attribute Utility Theory
MAVT Multi-Attribute Value Theory
MCDM Multi Criteria Decision Making
MODM Multi-Objective Decision Making
MOORA Multiple-Objective Optimization by Ratio Analysis
MULTIMOORA Multiple-Objective Optimization by Ratio Analysis plus the full MULTIplicative form
OWPS Offshore Wind Power Station
p-ELECTRE Probabilistic ELECTRE
PROMETHEE Preference Ranking Organization METHod for Enrichment of Evaluations
RES Renewable Energy Sources
RTUs Remote Terminal Units
SAW Simple Addictive Weighting
SHP Small Hydropower
SWOT Strengths, Weaknesses, Opportunities, and Threats
TOPSIS Technique for Order of Preference by Similarity to Ideal Solution
V2G Vehicle-to-Grid
VIKOR VIseKriterijumska Optimizacija I Kompromisno Resenje
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 783
review papers related to MCDM to highlight the gaps in this
field and to underscore the importance of conducting this
work. Recent review papers on MCDM (published between
2018 and 2021) have mostly examined a specific field or
area of application. By contrast, this paper covers several
fields where MCDM is implemented.
Fig. (1) presents those MCDM methods that are widely
applied in different fields. Each method has unique charac-
teristics and can be implemented in a broad area of interest.
Other known MCDM methods that are not listed in Fig. (1)
include the scoring approach that uses Simple Addictive
Weighting (SAW) and Complex Proportional Assessment
(COPRAS), the utility/value approach that uses Multi-
Attribute Utility Theory (MAUT) and Multi-Attribute Value
Theory (MAVT) [11], Goal Programming, Decision Making
Trial and Evaluation Laboratory (DEMATEL) and Grey
Relational Analysis (GRA). Whilst these methods are not
standalone, they generate promising results when combined
with the main MCDM methods. In recent years, researchers
have been using hybrid MCDM methods to obtain superior
outputs [20-22]. These methods are often combined with
fuzzy or grey sets [23, 24] and other approaches, such as
machine learning [25, 26] and geographical information sys-
tems (GIS) [27, 28]. Combining MCDM with fuzzy theory
[29] can overcome the limitations of the traditional MCDM
approach in certain situations. Fuzzy logic can also effective-
ly address those uncertainties arising from complex decisions
[30]. Several terms that are commonly used when imple-
menting MCDM are listed in Table 2 and will be used ac-
cordingly throughout the rest of this paper.
A total of 213 articles were initially identified from vari-
ous databases. Duplicate records were removed, leaving 194
articles to be screened. During the screening, 80 records
were excluded mainly due to out-of-scope or outdated arti-
cles. Finally, 114 papers are deemed to be fit and were in-
cluded in this paper. Fig. (2) shows the PRISMA flow chart
of the selection of studies.
The contributions of this paper are as follows:
• This paper provides a complete list of well-known
MCDM methods used in the industry and presents a
brief overview of each method.
• This paper reviews the related literature on MCDM
published between 2008 and 2021. The focuses and
contributions of each study are summarised in a table.
• This paper tabulates the advantages and disad-
vantages of each related MCDM method.
• This paper discusses the issues and challenges en-
countered in implementing MCDM methods.
The rest of this paper is organised as follows. Section 2
presents an overview of the MCDM literature. Section 3 ex-
amines the recent application of MCDM methods in several
fields. Section 4 highlights some issues and provides some
recommendations for future work. Section 5 concludes the
paper. Table 3 lists the acronyms used throughout the paper.
2. BACKGROUND STUDY
This section presents an in depth introduction and
discussion on the background, definition and steps involved
in pairwise comparison, outranking and distance-based
MDCM approaches.
2.1. Pairwise Comparison
2.1.1. Analytical Hierarchy Process (AHP)
DMs often face difficulties when choosing what is best
for reaching their goals. To overcome this problem, Saaty
[31] in 1986 proposed AHP, whose broad framework in-
volves the problem of the DM, the elements or criteria relat-
ed to his/her goals and the alternative solutions.
To develop priorities in an organised manner, decisions
should be made in the following manner [32]:
1. The problem and targets must be properly defined.
2. Several criteria and alternatives should be derived
from the specified problem.
3. The problem should be structured hierarchically as
shown in Fig. (3), where the goal of the decisions is
placed at the top, followed by the objectives and
problem criteria at the mid-level and the hierarchy
structure, usually comprising a set of alternatives, at
the lowest level.
Pairwise comparison matrices can be built by comparing
the element from the top level with those at the mid and bot-
tom levels. The fundamental scale of importance is then used
to evaluate the weightage of the criteria in pairwise compari-
sons. The scale of importance is summarised in Table 4.
To evaluate the consistency of the evaluation, the con-
sistency ratio (CR) is calculated by dividing the consistency
index (CI) by the random index (RI). The CR should be less
than 10%. If this percentage is exceeded, then the procedure
should be repeated to improve consistency.
(1)
The main advantages of AHP lie in its ease of use and
scalability, given that the weightage can be calculated by
using Internet software, including MakeItRational, Transpar-
ent Choice, SpiceLogic, Decerns MCDA, MATLAB, R and
Super Decisions, or by using an Excel template as stated in
another study [33]. The overall hierarchy structure clarifies
the importance of each element (criteria and alternative).
Given its suitability for low-complexity problems, AHP is
considered a convenient and straightforward approach.
However, AHP [34] prefers only 7 ± 2, or approximately
5 to 9 elements (criteria) based on Saaty’s scale of im-
portance. Although the number of hierarchy levels in AHP is
limited, the same capacity limit should be followed to avoid
creating a gigantic hierarchy structure [34]. The authors in
another study [35] argued that most companies do not prefer
AHP due to its inability to provide good rankings with a few
number of options.
2.1.2. Analytical Network Process (ANP)
To address the above limitations, researchers have pro-
posed ANP as an extension of AHP [36]. The network struc-
ture form of ANP allows DMs to make decisions under
complex conditions [11]. Unlike in AHP, the elements
784 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
Fig. (3). Overall view of the AHP hierarchy structure.
Table 4. The fundamental scale of importance.
Scale of Importance Definitions Explanations
1 Equally Significant Equality of two values
3 Slightly Significant The value is slightly important than the other
5 More Significant The value is preferable than the other
7 High Significant The value is strongly preferable than the other
9 Very High Significant The value is preferable than the other
2,4,6,8 Values between 1,3,5,7,9 According to explanations in 1,3,5,7 and 9
(decision criteria and alternatives) in ANP are independent
of one another. However, in some real-life problems, interac-
tions and dependencies between the top- and low-level ele-
ments are necessary. In this case, ANP is more efficient than
AHP by promoting a relational dependency amongst the el-
ements. However, ANP requires long brainstorming sessions
and involves a lot of expertise among the people who will
implement the method. Another drawback is the complexity
of ANP which requires the use of additional software, such
as Super Decisions, Expert Choice and Decision Lens. Fig.
(4) illustrates the ANP network structure, where some ele-
ments are dependent on other elements or clusters.
In ANP, the elements (criteria, sub-criteria and alterna-
tives) are referred to as nodes that can be compared with one
another if they are related. Unlike in AHP, the alternatives in
ANP depend on the weighting of criteria. ANP also provides
additional functions that allow the alternatives to influence
the ranking of criteria.
An ANP model is developed in four steps as follows
[37]:
1. The problem should be clearly structured similar to
a network.
2. Pairwise comparison is performed.
3. A super matrix is built (concept similar to the Mar-
kov chain process).
4. The criteria are synthesised with the priorities of al-
ternatives, and the best alternatives are selected.
Fig. (4). The network structure of ANP method.
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 785
One advantage of ANP over AHP is that independence
amongst elements is not required due to its network struc-
ture. Moreover, ANP can improve the priorities via feed-
back, thereby enhancing the accuracy and effectiveness of
predictions. However, ANP may not support uncertainty in
information. Due to its complexity, ANP also requires addi-
tional software, which may extend the time needed to solve
problems. Some available software for ANP include
ANPSolver, SuperDecision and other mathematical software
such as Excel, Maple and Mathematica.
2.2. Outranking
2.2.1. ELimination Et Choix Traduisant la REalité
(ELECTRE)
By taking into account both the min and max directions,
ELECTRA is considered one of the best outranking methods
in MCDM [38]. Although some variants of ELECTRE are
being applied depending on the type of problem, they all
describe the same concepts. Some of these variants include
ELECTRE I, ELECTRE Iv and ELECTRE IS for solving
choice problems, ELECTRE II, ELECTRE III, ELECTRE
IV and SS for solving ranking problems and ELECTRE TRI
for solving sorting problems. These variants are listed in
Table 5.
ELECTRE I and its modified variants, ELECTRE Iv and
ELECTRE IS, are most suitable for solving choice problems
[39]. In these approaches, the DM needs to select the best
choice in the smallest available subset [40]. In ELECTRE I,
all actions of the DM will form a cycle, which is considered
indifferent and thus inconvenient [41]. ELECTRE IS is a
generalisation of ELECTRE Iv that addresses the inconven-
ience brought by ELECTRE I. To solve ranking problems,
the authors [42] proposed ELECTRE SS, which can assess
the reliable performance of alternatives unlike ELECTRE
III. A ranking problem involves the ranking of all actions in
a given set from best to worst. A sorting problem involves
the classification of data, objects or items in a categorical set.
The authors [43] compared two MCDM methods, namely,
TOPSIS and ELECTRE TRI, of which the former works in a
continuous manner and the latter works in a discreet manner.
Unlike TOPSIS, ELECTRE TRI considers the uncertainty
and vagueness of both qualitative and quantitative criteria.
The authors in another study [9] argued that ELECTRE is
grounded on three factors, namely,
1. Determination of threshold function.
2. Concordance index and discordance index.
3. Outranking degree.
Despite its advantages, ELECTRE consumes a lot of
time. Some software that use ELECTRE include Deci-
sionLab, ChemDecide, MATLAB, R, Python, XLSTAT, J-
Electre and SMAA-TRI.
2.2.2. Preference Ranking Organization METHod for
Enrichment of Evaluations (PROMETHEE)
PROMETHEE is a famous MCDM outranking approach
introduced in 1982 by Professor Brans [13]. Similar to
ELECTRE, PROMETHEE has several variants, including
PROMETHEE I and II for partial and complete ranking
problems, respectively. PROMETHEE III and IV were later
developed by J.P. Brans and B. Mareschal based on interval
and continuous cases, respectively [13]. Years of research
have led to the discovery of further PROMETHEE exten-
sions, including PROMETHEE V in 1992 and PROME-
THEE VI in 1994, also proposed by J.P. Brans and B.
Mareschal [13]. PROMETHEE V is specifically tailored for
problems with segmentation constraints, whereas PROME-
THEE VI addresses human brain representation [7]. Fig. (5)
shows the structure of the PROMETHEE family.
J. P. Brans, P. Vincke and B. Mareschal [44] compared
the stability of ELECTRE III and PROMETHEE and found
that the latter is more stable due to the discontinuities in the
preference functions or derivatives of ELECTRE III. Similar
to ELECTRE, PROMETHEE also deals with both qualita-
tive and quantitative criteria. However, as an advantage,
PROMETHEE expresses these criteria in its own units and
requires fewer inputs, thereby reducing complexity and facil-
itating the use of this approach. However, if many criteria
and options are available, then the outcomes of PROME-
THEE may be difficult for the DM to evaluate. PROME-
THEE may also suffer from the rank reversal problem when-
ever a new alternative is introduced.
As summarised [9], the standard steps in PROMETHEE
are as follows:
Table 5. The variants of ELECTRE method.
Problem Method
Choice Problem
ELECTRE I
ELECTRE Iv
ELECTRE IS
Ranking Problem
ELECTRE II
ELECTRE III
ELECTRE IV
ELECTRE SS
Sorting Problem ELECTRE TRI
786 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
1. Find the evaluation matrix and perform a pairwise
comparison.
2. Assign a preference function with values ranging
from 0 to 1 depending on the difference between
pairs.
3. Calculate the global matrix and obtain the rank.
PROMETHEE is considered a user-friendly outranking
approach that has been widely used in addressing real-life
planning problems due to the completeness of its ranking
[45]. Several software for PROMETHEE is available, in-
cluding Decerns MCDA, Virtual PROMETHEE, D-Sight,
MATLAB, R and Smart Picker Pro.
2.3. Distance-based
2.3.1. Technique for Order of Preference by Similarity to
Ideal Solution (TOPSIS)
TOPSIS is a distance-based method proposed by Hwang
and Yoon in 1981 [14]. The overall concept of this method is
that the best alternative should have the nearest distance
from the ideal solution and the farthest distance from the
negative ideal solution [46]. Ideal and negative ideal solu-
tions are defined as follows:
Ideal solution: An ideal solution should have the best at-
tribute values, maximise the benefit criteria and minimise the
disadvantages criteria [11].
Negative ideal solution: A negative ideal solution should
have the worst attribute values, maximise the disadvantage
criteria and minimise the benefit criteria [11].
TOPSIS is implemented in the following steps [47, 48]:
1. Establish a standardised decision matrix and normal-
ised decision matrix, where J is denoted as alternatives.
and
(2)
where and are an original and normalized score of
the decision matrix, respectively.
2. Calculate the weighted normalised decision matrix as
(3)
where is the weight of the i-th attribute or criterion
and,
(4)
3. Determine the ideal solution, A* and negative ideal so-
lution, A-
The A*and A-defined in terms of the weighted normal-
ized values.
(5)
(6)
where, I’ is associated with benefit criteria, and I” is asso-
ciated with cost criteria.
4. Construct a separation between the ideal solution and
negative ideal solution using the n-dimensional Euclidean
distance.
*=
*
, Ideal solution (7)
-=
-
, Negative solution (8)
5. Determine a relative closeness to the ideal solution.
The relative closeness of the alternative aj with respect to
A*is defined as
Fig. (5). The structure of PROMETHEE family.
PROMETHEE
MAIN
EXTENSION
PROMETHEE I
PROMETHEE II
PROMETHEE III
PROMETHEE IV
PROMETHEE V
PROMETHEE VI
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 787
(9)
6. Rank the order of preferences.
The advantages of TOPSIS lies in its simplicity of usage
and easy programming. The steps involved in TOPSIS are
always the same regardless of the number of attributes.
However, by using Euclidean distance, TOPSIS does not
consider the correlation amongst the attributes. Moreover,
maintaining a consistent judgement is difficult in TOPSIS,
and this approach completely disregards the uncertainty in
weightings. Apart from Microsoft Excel, TOPSIS can be
used with some online software, including Decerns MCDA,
Scikit-Criteria, R, OnlineOutput and MATLAB.
The authors in another study [49] compared ELECTRE,
TOPSIS and Grey Theory and found that TOPSIS is mainly
used for ranking, ELECTRE is mainly used for selection and
Grey Theory is mainly used in cases where no data are avail-
able.
2.3.2. VIseKriterijumska Optimizacija I Kompromisno
Resenje (VIKOR)
Initially called Multicriteria Optimisation and Compro-
mise Solution, VIKOR was proposed by Opricovicfor in
1979 and first applied with Duckstein in 1980 [50]. Howev-
er, the name VIKOR was only used starting in 1990. This
technique is mainly used to evaluate and compare the sus-
tainability of various energy plans or renewable energy tech-
nologies to provide decision support for selecting the most
sustainable and appropriate options [51]. The differences
between TOPSIS and VIKOR were studied [48], where the
authors found that both methods use different types of nor-
malisation to eliminate the units of criterion functions. VI-
KOR uses linear normalisation, whereas TOPSIS uses vector
normalisation. The authors also highlighted other features
that distinguish these two methods, such as their procedural
basis, aggregation and solution. Different normalisation ap-
proaches produce different aggregating functions for ranking
[48].
The procedures in VIKOR can be summarised as follows
[48, 52]:
1. Calculate the best and the worst
Where, is the value of the i-th criterion function for
the alternative fi
(10)
2. Compute Sj and Rj
Where Sjand Rjdenote the utility measure and regret and
denotes the weights of the criterion, indicating their rela-
tive importance.
(11)
(12)
3. Compute S* and R*.
,
, (13)
4. Determine value of , where and rank the
alternatives.
(14)
where v is weight of maximum group utility, and 1-v is
the weight of individual regret. v is ideally equivalent to 0.5.
When v > 0.5, Qj indicates majority agreement, and when v <
0.5, Qj indicates majority negative attitude.
Similar to other MCDM methods, VIKOR can be inte-
grated or modified to obtain better results for specific appli-
cations. Variants of VIKOR include the fuzzy VIKOR for
water resources planning [53], modified VIKOR for improv-
ing the service quality of domestic airlines [54], interval VI-
KOR [55], comprehensive VIKOR for selecting femoral
components in total knee replacement [56] and regret-theory-
based VIKOR [57]. Amongst these variants, the interval and
regret-theory-based VIKOR demonstrate the worst perfor-
mance [58]. Fuzzy VIKOR is generally preferred in cases
where information about the decision problem is imprecise.
However, in some cases [59], the original VIKOR is a much
better option given the low complexity in its mathematical
computation. A new version of VIKOR called VIKORRUG
was proposed [60] as a compromise-ranking algorithm that
allows DMs to further improvise any objections and rank the
unimproved gaps in the alternatives.
Similar to TOPSIS, the advantages of VIKOR lie in its
simple process and consistent number of steps regardless of
the number of attributes. VIKOR provides additional
strength with its capability to maximise the utility group and
minimise the regret group. However, VIKOR suffers from
drawbacks in its performance rating, which is quantified as a
crisp value that is generally inadequate in modelling real-life
situations. To address this drawback, DMs need to consider
imprecise or ambiguous data. VIKOR can be used with Ex-
cel, R, MATLAB and OnlineOutput.
3. REVIEW OF APPLICATIONS FOR ALL METHODS
MCDM is used in various types of DM problems, such as
location or site selection, supplier selection, project assess-
ment, method or procedure selection, maintenance or config-
uration planning, energy resources selection and social as-
sessment, and covers many industries from energy, sustaina-
bility, transportation, manufacturing and production. This
section exhaustively reviews the applications of MCDM
across various fields.
=
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3.1. Pairwise Comparison Methods
The AHP has been used in various applications, such as
selection, cost-benefit analysis, forecasting, evaluation, DM,
priority and ranking and planning and development, and in
several major fields, including medical and healthcare [61,
62], business [63-65], transportation [66, 67] and renewable
energy [68, 69]. The AHP is commonly used in selecting
locations for power plants around the world [20, 27, 28, 70]
and in measuring the performance and configurations of mi-
crogrids [71], including their load shedding and optimisation
cost. The authors in another study [72] identified a few chal-
lenges associated with islanding detection method (IDM)
selection, which greatly depends on the type of distributed
generator (DG) units and their connection topologies. The
lifetime, location and future expandability of DGs and utility
grids are some of factors associated with IDM selection. The
AHP method is used in a grid-connected photovoltaic system
(GCPVS) to help the IDM provide a complete solution for
anti-islanding. The authors proposed a passive/active hybrid
method, with the passive method measuring the system pa-
rameters and the active method introducing perturbations in
system quantities. However, the AHP faces some limitations
related to the weightings of DMs because DMs often en-
counter difficulties in expressing their preferences based on
the defined ration scale, but these challenges are easily over-
come by using group DM tools, such as consensus.
In energy planning, the AHP is often combined with oth-
er methods. For instance, the AHP may be used to weigh the
criteria, and then the DM will apply Multiple-Objective Op-
timisation by Ratio Analysis and the full MULTIplicative
form (MULTIMOORA) method to sort the alternatives [73].
MOORA is an MCDM method developed by Brauers and
Zavadskas in 2006 [74], where each response of an alterna-
tive per objective is compared with the square root of the
sum of squares of the responses. As an extended version of
MOORA proposed in 2010, MULTIMOORA [75] summa-
rises the ratio scheme, point of reference and complete mul-
tiplication form used in MOORA [76]. The authors in anoth-
er study [73] determined the best configuration plans to
achieve an optimum planning of microgrids. These plans
included several combinations of energy resources, such as
wind turbine, biomass and diesel, and considered three load-
ing conditions (high, medium and low). Another energy
planning research from Turkey combined ANP with fuzzy
TOPSIS and Strengths, Weaknesses, Opportunities, and
Threats (SWOT) analysis [77]. Specifically, ANP and fuzzy
TOPSIS were combined to overcome the overall complexi-
ties of the DM process, and the SWOT framework was sub-
sequently integrated to create a strategic plan. The authors
used ANP to weigh each SWOT factor and subfactor and
found that the usage of fossil fuels can be reduced by deploy-
ing other renewable energy resources, such as nuclear, geo-
thermal, solar and wind energy.
AHP can also be used in maintenance planning, such as
in measuring the health of substations by integrating multiple
criteria for equipment conditions. This method was proven
effective in assessing the conditions of 74 substations in the
Tokyo Electric Power service areas [78]. The effectiveness
and practicality of AHP were evaluated within five years
using its developed procedure. The condition indices were
compared across three regions, namely, urban, suburban and
remote regions, with the urban region having the smallest
indices, followed by the suburban and remote regions (a
smaller index is generally preferred). These results were then
validated by frontline engineers responsible for maintenance
planning. The AHP also allows DMs to predict the future
health conditions of substations. Meanwhile, the authors
used the fuzzy-AHP model in selecting maintenance policies
in the manufacturing industry [23]. Faced with increasing
competition in their markets, companies should acknowledge
the progressive maintenance system of their competitors.
AHP selects maintenance strategies based on seven types of
equipment that are mainly used in the chemical industry,
especially by manufacturers of sulfuric acid. The main goal
of this approach is to reduce the overall costs and risks whilst
increasing the added value for the manufacturer.
Another difficult DM problem is selecting a site or loca-
tion for various projects, such as smart grids, power plants
and transportation midi-hubs. Both AHP and ANP are often
used to this end. Whilst some practitioners used either AHP
or ANP, others developed a hybrid approach, especially
when using either AHP or ANP alone cannot solve the speci-
fied problem. In a case study involving the selection of an
Algerian network, the authors used AHP to prevent the cas-
cading degradation of electrical networks and cascading
blackouts that can lead to losses [79]. In this work, AHP was
employed as a DM tool to solve complex problems in issuing
judgments based on past experience and informal data. Sev-
eral scenarios around the world, including those in the US,
China, India, Italy and Germany, were also reviewed to se-
lect the best scenario for Algerian smart grid projects. The
decision criteria included service continuity, power quality,
energy efficiency, control of peak demand, transportation
over long distances with minimal losses, integration of re-
newable sources with minimum investment, decentralisation
and launching of electric vehicles, development of clean
energy and environment protection. The authors highlighted
the value of learning from the actions and goals of projects
conducted in the US, Italy and Germany. In another work,
the authors used GIS and AHP to find a suitable location for
a solar PV power plant in Saudi Arabia [27]. The AHP has
been widely applied in planning RES, especially for location
selection whilst taking into consideration several aspects,
such as economic, environmental and technical factors. They
integrated GIS with AHP in consideration of 14 criteria and
evaluated the importance of each criterion using AHP. A
similar approach was adopted by the authors [28] in selecting
the best location for a wind farm. They considered several
parameters, such as environment and climate change, to de-
termine the optimal wind farm location and harnessing of
wind energy. Konstantinos et al. [70] used a hybrid of GIS-
AHP and TOPSIS for the site selection of a wind farm in
Greece. This hybrid tool could help policymakers determine
optimal installation sites whilst considering several critical
parameters, such as social and public attitudes, which play
important roles in the selection of installation sites. Some
other parameters that need to be critically reviewed given
their potential effects on the installation location include
accessibility, current land use and legislation frameworks.
The authors [80] studied some alternatives for selecting
the location of a solar power plant in Turkey. They found
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 789
that using ANP was a lengthy endeavour, given its consider-
ation of the interdependencies across and along the decision
hierarchies. However, this approach provides a more reliable
solution compared with AHP. An ANP-based methodology
also considers quantitative and qualitative elements when
evaluating location alternatives. The authors applied ANP
using Microsoft Excel, given that they only considered a
limited number of elements. ANP also can be integrated with
other methods, such as GQM and fuzzy DEMATEL [81],
where the authors focused on those key factors that affect
wind farm location selection and wind energy accumulation,
such as wind sources, accessibility of electrical grids and
population distribution. They applied ANP for weighing the
dimensions and criteria and used fuzzy DEMATEL to ascer-
tain the effects of these criteria and their relative intensities.
When using ANP, the authors considered 6 factors associat-
ed with 28 evaluation criteria. From the ranking of these
criteria, they identified regular wind farm testing, secure
setup distance and destroyed windbreaks as the key criteria
for practitioners and environmental ecology monitoring, de-
stroyed windbreaks and regular wind farm testing as the key
criteria for academics. In other words, practitioners tend to
focus on the safety of wind turbines, whereas academics fo-
cus on data monitoring and environmental issues. Safety and
Quality, together with Environment and Ecology, emerged as
the major factors that affect the wind farm location selection.
A recent study [82] identified those factors that prevented
the development of renewable technologies in Pakistan and
proposed some methods for overcoming these barriers. The
authors employed the hybrid AHP-fuzzy TOPSIS approach,
with AHP assessing the criteria and sub criteria and fuzzy
TOPSIS evaluating the strategies for the long-term imple-
mentation of renewable energy technologies. The authors
initially determined the potential barriers to the implementa-
tion of renewable technologies, including 7 main barriers and
29 sub-barriers. From their list, they formulated 10 strategies
for addressing the identified barriers. The most suitable strat-
egy obtained from fuzzy TOPSIS was capital subsidies,
which may assist the Pakistani government and DMs in
overcoming the obstacles to achieving sustainable energy
planning and development in their country. Another recent
study in Vienna employed AHP for site selection in the trans-
portation field [67]. The authors aimed to select a location that
is suitable for a midi-hub or a medium-size city hub. They
discussed their criteria with DMs and then weighed them by
using AHP given that this approach takes the judgment of
DMs into account. Suitable midi-hub locations were then de-
termined based on the indicators defined for each criterion,
and then these locations were evaluated in a pairwise manner.
Other than site selection, ANP was also used to deter-
mine the suitability of power plants in Turkey in response to
the growing demand for electrical energy driven by the in-
creasing population and industrial growth in the country
[83]. The authors aimed to determine the most suitable pow-
er plant from six types of widely used power plants (i.e. nu-
clear, geothermal, wind, natural gas, hydroelectric and
coal/lignite). They evaluated the future installation of these
power plants in Turkey whilst considering several important
criteria, such as technology, sustainability, life quality, eco-
nomical suitability and socioeconomic impacts correspond-
ing to the energy needs of the country. They found that the
ANP was particularly suitable for complex DM environ-
ments given that this approach allows feedback amongst the
elements. The ranking decision results show that nuclear
power plants are most suitable for Turkey, with coal/lignite
power plants being the most unsuitable given their low re-
serves-to-production ratio and high radioactivity.
A recent case study in China [84] proposed the applica-
tion of fuzzy ANP for wind turbine selection in response to
the increasing variety of wind turbines available in the mar-
ket. The selection was based on three aspects, namely, opera-
tion reliability, economy and supplier cooperation. A total of
12 indicators were evaluated, and the authors proposed a fuzzy
ANP model to evenly distribute the weight of the evaluation
index and facilitate the selection of the best wind turbine. Op-
erational reliability emerged as the most important indicator
for the selection, followed by economic indicators and availa-
bility of wind turbines. The G100-2.5 MW unit wind turbine
produced by Goldwind Company was chosen as the most suit-
able alternative due to its permanent magnet direct-driven
generator with excellent performance and low maintenance.
The authors added that the FANP model may help evaluate
other renewable energy equipment by changing indicators.
However, one downside of the hybrid fuzzy model is that the
calculations tend to be increasingly complex, thereby adding
to the workload in the assessment phase.
The project assessment in another study [20] integrated
AHP and ANP to check the profitability of investing in a
solar thermal power plant project. The authors proposed a
three-level decision. At level 1, they evaluated whether using
AHP is worthwhile in assessing the acceptability of a solar-
thermal power plant project. At Level 2, they analysed the
accepted projects by using a new set of criteria and a deeper
knowledge of these projects. At Level 3, they defined new
criteria for assessing the project risks. The decision alterna-
tives are the different projects of the portfolio. The authors
proposed using both the AHP and ANP models in the as-
sessment. Whilst the AHP is conceptually easy to use, given
its strict hierarchical structure, the AHP cannot easily handle
complex real-world problems. Therefore, the authors resort-
ed to using ANP in handling complex problems, given that
its network structure can incorporate the feedback and inter-
dependence relationships within and between clusters. Ra-
ther than overtaking the decision of DMs, the proposed hy-
brid AHP/ANP model helped evaluate and analyse the out-
comes of the projects. However, relative to AHP, the ANP
could better reflect the complexity of a problem by allowing
DMs to compare the outcomes of various models and evalu-
ate their pros and cons. The authors [85] assessed a supply
chain by using an ANP-MOORA hybrid to identify and
evaluate issues in green supply chain management. Imple-
menting the ANP-based framework effectively reduced the
complexity of MCDM approaches, hence facilitating the
selection of the best supply chain.
Some challenges in the implementation of ANP require
pairwise group comparisons. Given the difficulty in achiev-
ing the optimal minimum and maximum values for each met-
ric, the usage of fuzzy numbers in this model is limited.
Moreover, the chosen approach often reflects the subjective
bias of a DM. Therefore, a restricted rationality bias may be
observed. For example, in the supplier evaluation and selec-
790 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
tion work of Giannakis et al. [86], they faced challenges in
measuring accuracy given that the overload measures are
biased towards heavy works and that very few alternatives
may not fit well. To address this problem, they developed a
framework that can be used across various industries for
evaluating and selecting suppliers that are tailored to the
industry needs. In the production field, the ANP is mainly
used in green supplier selection, where finding an effective
and reliable alternative is crucial.
This topic has received much attention from researchers
and practitioners due to growing concerns on the detrimental
impacts of businesses operations on the environment. The
authors [87] modelled a framework for evaluating the green
performance of an alternative (supplier) whilst considering
economic and environmental criteria. They employed the
Improved GRA (IGRA) to reduce the uncertainty from ANP.
The IGRA may also gather and integrate the judgement of
experts into the model, thereby enhancing its flexibility. The
authors also performed a sensitivity analysis on the decision
parameters to observe the changes in the order of suppliers
whenever some changes in scenarios are observed.
The above reviews on the application of AHP and ANP
are summarised in Table 6, and sorted according to the goal
of the projects and the field of alternatives. These approaches
are popular in selecting the locations of energy farm power
plants, and combining them with GIS presents a very prom-
ising direction for future work.
3.2. Outranking Based Methods
The site selection problem can also be solved by using
outranking-based MCDM methods. Selecting an appropriate
location for logistic facilities, such as distribution, plants and
collection centres, is an important and complex problem.
Therefore, many methods have been formulated to assist
DMs in making an appropriate choice. However, some of
these methods have limitations, as listed in another study
[38]. For example, these methods do not take the preference
of DMs into account and only deal with quantitative criteria,
such as transport costs, proximity to customers and connec-
tivity to multimodal transport. The importance of qualitative
criteria should be considered in the DM process given that
they cover several important factors, such as congestion lev-
el, customer satisfaction and safety. To overcome these limi-
tations, ELECTRE I was used in another study [38] to de-
termine the location of distribution centres given its capabil-
ity to improve DM by taking quantitative and qualitative
criteria into account. The selected solution was validated by
testing concordance and discordance simultaneously. The
authors found that ELECTRE I can be used in other sectors,
such as in logistic, biomedical and automatic sectors. Sen-
naroglu and Celebi [88] investigated the military airport se-
lection in Turkey by integrating PROMETHEE with AHP
and VIKOR. They built a two-level hierarchy of criteria,
where the first level comprises 9 main criteria, namely, mili-
tary, expansion potential, cost, environmental and social ef-
fects, climate conditions, infrastructure facilities, land, geo-
graphical features and needs, and the second level compris-
ing 33 sub-criteria. These criteria were selected from a litera-
ture review and through consultations with three DM ex-
perts. They eventually used AHP to subjectively determine
the weights of these criteria. In a comparative analysis, both
PROMETHEE and VIKOR identified location C as the best
alternative and outputted the same ranking for the alterna-
tives (C > A > D > B).
A site selection work using ELECTRE III was conducted
in the energy conversion field for the deployment of an off-
shore wind power station (OWPS). The authors [89] pro-
posed a framework that determines the best site in China for
deploying an OWPS with the aid of an intuitionistic fuzzy
set to handle uncertain and imprecise information. A robust
OWPS site selection index system comprising veto and as-
sessment criteria was also designed to exclude vulnerable
areas and classify possible alternatives. By implementing
ELECTRE III, any unclear information can be avoided or
outranked, and the importance of basic knowledge can be
utilised. The authors [90] combined fuzzy AHP and PRO-
METHEE with ArcGIS to select a suitable location for an
8,8000 MW solar farm in Bali, Indonesia.
This hybrid method came in the form of a toolbox in the
ArcGIS application that allows users to effectively determine
the best location and accelerate the generation of electricity
from solar energy in the case area by reducing the time need-
ed for analysing the suitability of land. Another study in Pa-
dang, Indonesia integrated the Borda method into PROME-
THEE to determine a suitable location for naval bases [91].
The authors implemented the Borda method by allocating
weights to each of the first, second and subsequent ranking
criteria. Amongst the 16 criteria considered, the Sailing
Channel obtained the largest weight, indicating its significant
impact on the choice of the base site. The criterion with the
second largest weight was Depth of Sea. with Political Con-
dition obtaining the lowest weight. The 16 criteria were
evaluated across three proposed base locations, namely, the
Semabuk Bay, Siuban and Semebai Bay. Results eventually
point towards Semebai Bay as the best location for the naval
base. The proposed model was deemed robust because the
effects of sensitivity analysis on the parameters and alterna-
tive weightings did not change along with the chosen alterna-
tive ranking result.
The authors [92] integrated DEMATEL-ANP (DANP)
into PROMETHEE to determine the best green manufactur-
ing (GM) practice and to optimise the use of valuable green
strategies. External drivers, such as pressures from stake-
holders, customers and general bodies, helped determine the
level of GM implementation in the context of developing
countries. They utilised DANP to analyse the dimensions
and criteria and avoid an irrational weighted super matrix.
As a result, DANP provides DMs with the best solution for
their respective industries. In another study, the authors at-
tempted to identify the optimum vehicle-to-grid (V2G) bat-
tery storage for electric vehicle users [93]. Although ELEC-
TRE has been successfully applied across various fields, the
authors adopted a probability approach called Probabilistic
ELECTRE (p-ELECTRE) instead of relying on direct scores
to evaluate the favourableness of an action based on instan-
taneous values of criteria. p-ELECTRE considers many pa-
rameters, such as SoC, electricity price, system load and
availability of renewable energy throughout the day. The
optimal dispatch was obtained whilst meeting the system
load and reducing losses, cost and voltage deviation. In a
distribution system, Kamble et al. [21] developed an impro-
vised AHP-PROMETHEE method, which provides a better
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 791
Table 6. The summary of previous work on AHP and ANP methods.
Authors Goal Field Method Description
Rietz and Suryanarayanan,
2008 [71] Identify procedure Energy/ Microgrids AHP Used for determining the various procedure for
microgrids such as load shedding
Datta et al., 2014 [72] Identify method Energy AHP Used to identify the islanding detection meth-
ods in a GCPVS
Moravej and Afshar, 2014
[73] Energy planning Energy/Microgrid AHP
MULTI MOORA
Used to find the possible configuration plans
that include several combinations of energy
resources for the microgrids.
Ervural et al. 2018 [77] Energy planning Sustainability
ANP
fuzzy TOPSIS
SWOT Analysis
A case study in Turkey on how to reduce ener-
gy dependence on foreign sources by deploying
a framework to determine energy resources
strategies.
Tanaka et al. 2010 [78] Maintenance planning Energy/Substation AHP
To evaluate the substation maintenance plan-
ning in Tokyo where the substation is classified
into three different areas: urban, suburban and
remote areas.
Hemmati et al., 2018 [23] Maintenance planning Manufacturing fuzzy ANP
Case study of a sulfuric acid manufacturer in
determining the suitable maintenance strategies
based on several types of equipment.
Iberraken et al., 2013 [79] Scenario selection Energy/Smart grid AHP
Using other countries as the alternatives for the
best scenarios for the Algerian smart grid pro-
ject
Al Garni and
Awasthi, 2017 [27] Site selection Energy/Solar PV Power Plant AHP
GIS
Taking the case study in Saudi Arabia to find
the most suitable location to implement the
solar PV power plant
Baseer et al., 2017 [28] Site selection Energy/Wind Farm AHP
GIS
A case study in Saudi Arabia to determine the
optimal wind farm location and harnessing
wind energy
Konstantinos et al., 2019
[70] Site selection Energy/Wind Farm
AHP
TOPSIS
GIS
A case study in Eastern Macedonia and Thrace
region, Greece to determine the wind farm
installation location based on several criteria.
Ayag and Samanlioglu,
2010 [80] Site selection Energy/Solar Power Plant ANP
To determine the solar power plant location by
taking into consideration of quantitative and
qualitative elements.
Yeh and Huang, 2014 [81] Site selection Energy/Wind Farm
ANP
GQM
fuzzy DEMATEL
Taking consideration of six factors in finding
the wind farm location by having a total of 28
evaluation criteria.
Y. A. Solangi et al., 2021
[82] Project assessment Renewable Energy AHP
Fuzzy TOPSIS
To overcome the renewable energy develop-
ment barriers by finding the best strategy
Anderluh et al., 2020 [67]
Site selection Transportation AHP
The selection of the location in midi-hub con-
sider as one of the challenges thus requires a
support tool to assist the DMs
Atmaca and Basar, 2012
[83] Power plant selection Energy/Power Plant ANP
To find suitable power plants in Turkey that
can provide a supply-demand balance based on
several criteria.
Beltrán et al., 2014 [20] Project assessment Energy/Thermal Power Plant AHP
ANP
Determine whether it is profitable to invest in
solar thermal power plant project
(Table 6) contd…
792 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
Authors Goal Field Method Description
Pang et al,. 2021 [84] Product selection Renewable Energy fuzzy ANP To find a suitable wind turbine for case study
in China
Chand et al., 2018 [85] Project assessment Manufacturing ANP
MOORA
To evaluate and analyse the selected issues in
green supplier chain management and help the
DM to evaluate the impact of different types of
supply chains
Giannakis et al., 2020 [86] Supplier selection Sustainability ANP
To evaluate and select suppliers by developing
a framework that can be utilised in various
industries.
Hashemi et al., 2015 [87] Supplier selection Production ANP
IGRA
A selection of green supplier in green supply
chain management
evaluation of alternatives for the selection of configuration in
a distribution system. The first step in this method was to
short-list alternatives based on identified criteria and then
prepare a decision table that presents the measurements or
values of all criteria with a focus on minimising losses. Af-
terward, the preference function would translate the differ-
ence between the evaluations obtained by several alternatives
based on a particular criteria into a preference degree ranging
from 0 to 1. AHP-PROMETHEE allows DMs to systemati-
cally assign values of relative importance to several criteria
according to their preferences.
A similar work was performed in China to predict the fu-
ture cost of using fossil fuels as the main energy source [94].
The authors examined whether a rise in electricity usage
would increase or deplete fossil fuels and necessitate the
identification of alternative energy production methods. As a
case study, they used PROMETHEE II to select a suitable
location for the Parabolic Trough Concentrating Solar Power
Plant in China based on several criteria. However, concen-
trating solar electricity generation encounters several ob-
structions, such as low efficiency and large land cover. The
authors adopted PROMETHEE II instead of TOPSIS given
the unavailability of various functions in the latter, making
this approach unable to deal with a flexible and diverse DM
process. The authors also compared fuzzy VIKOR with
fuzzy ELECTRE and found that fuzzy PROMETHEE II
provides the most accurate and creditable ranking sequence.
In the field of sustainable energy, the authors used ANP,
fuzzy PROMETHEE and TOPSIS to evaluate whether power
plants are ideal investment projects in response to the in-
creasing usage of renewable energies and to determine which
renewable energy resource is worthy of investment in case
the cost of fossil fuel rises in the future [95]. Their study
covered both environmental and social aspects, which are
fundamental in investment prioritisation. Other factors con-
sidered included the final production cost of thermal power
plants and the capacity of wind and solar power plants. The
authors found that both fuzzy PROMETHEE and fuzzy
TOPSIS provide similar rankings of power plants.
The authors in another study [45] used PROMETHEE to
investigate the effect of different preference functions for a
green supplier selection problem in Malaysia. They started
out by evaluating a set of alternatives with respect to the
criteria where the first net outflows and preference order
were obtained by adopting the usual criterion preference
function. The second net outflows and preference order were
then acquired by adopting a combination of the linear and
level preference functions. Both of these functions were cho-
sen based on the nature of their criteria. The authors stated
that previous literature on PROMETHEE did not fully dis-
cuss the effect of preference functions on the final preference
order. In their case study of green supplier selection involv-
ing four alternatives, five DMs and seven criteria, the authors
concluded that such effect is not significant. The authors in
another study [24] evaluated the performance of green sup-
pliers by using the fuzzy extended ELECTRE to ensure sus-
tainable systems. They prepared a fuzzy decision matrix us-
ing the weighted average of ratings of suppliers against vari-
ous attributes. A total of 38 factors were considered and
grouped into 6 clusters, namely, cost, quality, flexibility,
service, green practice, environmental management and pol-
lution control. The authors focused on outranking the poor
performers and only considered the comparable performers
in the ranking. Their findings could benefit other work relat-
ed to supplier selection that focuses on green production and
practices, such as solid waste, chemical waste, air emissions,
water waste and other environmentally friendly materials and
methods.
Assessing sustainable cities is critical for understanding
the state of urban growth in real time and making policy
changes. In a case study of China [96], the authors proposed
an ELECTRE model for assessing the level of urban sustain-
ability across the key dimensions of economy, society and
environment. A total of 17 cities in Henan Province were
selected for the case study between 2013 and 2017. Amongst
these cities, Zhengzhou reported the highest degree of eco-
nomic sustainability, and Shangqiu ranked the lowest.
Zhengzhou also ranked first in the social dimension, with
Zhoukou ranking last. Meanwhile, in the environmental di-
mension, Zhumadian reported the highest sustainability,
whereas Jiaozuo reported the lowest sustainability. Zheng-
zhou, as the city with the highest sustainability, invested
large amounts of its capital in economic, social construction
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 793
Table 7. The summary of previous work on ELECTRE and PROMETHEE method.
Authors Goal Field Method Description
Agrebi et al., 2017
[38] Site selection Transportation ELECTRE I Selection of a possible location to optimize the
logistic system for the distribution centre
Sennaroglu and
Celebi, 2018 [88] Site selection Transportation
PROMETHEE
AHP
VIKOR
Determination of the military airport location and
making comparisons with other methods
Wu et al., 2016 [89] Site selection Energy/Wind Power Station IF-ELECTRE III Develop a framework to decide the site selection for
the offshore wind power station in China
Wiguna et al., 2018
[90] Site selection Energy
PROMETHEE
fuzzy AHP
ArcGIS
The case study in Bali on integrating methods to
find the most suitable places for the solar farm
Ahmadi and D.
Herdiawan , 2021 [91] Site selection Marine Borda
PROMETHEE
To find the suitable location for dock and office of
naval bases
Govindan et al., 2015
[92] Method selection Production DEMATEL-ANP
PROMETHEE
The GMP requires some efficient and valuable
practices for the DMs to select the best solution
Yammani et al., 2016
[93] Method selection Energy p-ELECTRE To find the optimum V2G battery storage for the
electric vehicles user
Kamble et al., 2018
[21] Method selection Energy PROMETHEE
AHP
Development of a configuration for the distribution
system to minimize the losses that occur.
Wu et al., 2019 [94] Method selection and
site selection Energy PROMETHEE
TIFGOWA
The selection of solar power plant using PROME-
THEE II specifically and compared with other
MCDM methods
Tabaraee et al., 2018
[95] Project selection Sustainability ANP
fuzzy PROMETHEE Identification of the best renewable energy solution.
Abdullah et al., 2019
[45] Supplier selection Manufacturing PROMETHEE Used for green supply selection by using seven
criteria in economic and environmental factor
Li et al., 2021 [96] Sustainability evalua-
tion Sustainability ELECTRE
To evaluate the urban sustainability based on 17
cities in Henan Province
Kumar et al., 2017
[24] Supplier assessment Sustainability fuzzy ELECTRE To evaluate the performances of green suppliers
Polatidis et al., 2015
[97] Method assessment Energy/Geothermal Energy ELECTRE III
PROMETHEE II
Comparison of two outranking technique on analysis
for the geothermal energy
Dias et al., 2018 [98] Policy assessment Energy/Smart Grid Delphi
ELECTRE Tri
To create an assessment on sort and rank policies in
the smart grids in Brazil
Wu et al., 2017 [99] Social assessment Sustainability
PROMETHEE
HFLTS
ANP
Evaluation of the assessment of social sustainability
in China
794 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
and environmental protection. Meanwhile, those cities with
the lowest sustainability were usually located in regions with
concentrated cultivated land. In another work, the perfor-
mance of ELECTRE III in geothermal analysis was com-
pared with that of PROMETHEE II [97]. The authors aimed
to understand whether these methods could obtain conver-
gent results related to renewable energy development under
the same situation and determine whether a DM process can
be facilitated by using more than one method. The authors
converged both methods and used a real data set from a case
study. Their findings shows that both ELECTRE II and
PROMETHEE II obtained almost the same results because
they are both outranking-based MCDM methods.
The authors used ELECTRE TRI to sort and rank poli-
cies with the aid of the Delphi process [98]. Specifically, the
expert judgment of 28 stakeholders was obtained by con-
ducting a Delphi survey that aimed to elicit inputs about pol-
icy impacts and some main parameters of the ELECTRE TRI
model (criteria weights and veto thresholds). The case study
was conducted in Brazil with the goal of modelling an as-
sessment method for smart grids. ELECTRE TRI was used
to evaluate the merits of each policy without any relation to
other policies. The authors discovered that ELECTRE TRI
could not address potential synergies amongst policies given
that these policies were evaluated independently of one an-
other considering their intrinsic advantages and disad-
vantages. If a few policies need to be synergised, then
ELECTRE TRI can be used to evaluate composite policies
(e.g. policy P9 could represent the implementation of two
other policies at the same time), but using this approach to
evaluate all combinations would be impractical. Amidst the
growing awareness regarding sustainable energy develop-
ment, small hydropower (SHP) witnessed a rapid develop-
ment due to its small scale, low investment, low cost and
rapid effect. The authors [99] employed PROMETHEE and
the novel Hesitant Fuzzy Linguistic Term Set (HFLTS) to
assess the social sustainability of SHP in China. Their as-
sessment focused on self-interests that can be generated in
the construction and operation of SHP. For instance, some
operators may aim for public recognition, whereas others
may pursue SHP development to benefit the local residents.
The HFLTS can overcome the uncertainty and inaccuracies
in the evaluation and provide a full description of the fuzzi-
ness and randomness of criteria value information.
The previous work on ELECTRE and PROMETHEE as
reviewed in this section are summarised in Table 7 and sort-
ed according to their project goals and field of alternatives.
3.3. Distance Based Method
Distance-based methods have often been used to fulfill
many goals, such as energy planning, site selection, method
selection, supplier selection and project assessment. Similar
to other MCDM methods, these approaches are applicable to
many fields, including energy, sustainability, manufacturing
and production. For instance, power distribution planning
was investigated to optimise the network routing problem by
minimising the feeder routes subject to a set of constraints
[22]. The AHP-TOPSIS hybrid model was used in considera-
tion of several reliability aspects, such as miles of conductor,
feeder failures, customer interruption, maximum interruption
and estimated relative cost. The AHP was used to weigh the
criteria, and planning for the power distribution system was
carried out by using TOPSIS.
Sustainable development faces critical issues related to
the depletion of natural resources, deterioration of the eco-
logical environment and energy crises. One initiative geared
towards solving these problems is selecting the optimal sites
for electric vehicle charging stations using fuzzy TOPSIS,
which is deemed a robust and effective method [100]. How-
ever, some real-world problems cannot be solved using the
original TOPSIS because some criteria cannot be measured
by crisp value. The site selection is conducted from the sus-
tainability perspective and in consideration of environmental,
economic and social criteria and 11 other sub-criteria. The
authors [101] proposed a decision framework for a Large
Commercial Rooftop Photovoltaic System (LCRPS) site
selection by using a hybrid of fuzzy ANP and fuzzy VIKOR.
They proposed 5 alternatives across China (2 in Guangzhou,
2 in Dongguan and 1 in Foshan) [101] and focused on 5
main criteria (resource, economy risk, environmental and
social factors) and 16 sub criteria. However, their framework
development was restricted by the lack of complete infor-
mation that can contribute to the LCRPS site selection, a
problem that can be solved by using fuzzy VIKOR. The
AHP-fuzzy TOPSIS hybrid was also used in the field of re-
newable and sustainable energy. With the rising usage of
fossil fuels, researchers have attempted to reduce fossil fuel
intake by proposing other sources of renewable energy. A
case study in India reported that selecting a location for the
deployment of a solar farm needs to take into account the
efficiency of receiving solar resources [102]. They highlight-
ed the importance of social, technical, economic, environ-
mental and political factors in the site selection. Another
study planned the location of a solar power plant in Vietnam
by integrating fuzzy AHP (FAHP), data envelopment analy-
sis (DEA) and TOPSIS [103]. The authors eventually identi-
fied 46 potential locations using several DEA models, em-
ployed FAHP to weigh the factors and applied TOPSIS to
rank the locations.
AHP-VIKOR was used [59] to identify renewable energy
projects in Spain that are worthy of investment. This hybrid
approach allows DMs to rank the importance of attributes
based on their preferences. The studied renewable energy
plan involved several energy sources, including wind power,
hydroelectric, solar thermal, solar thermo-electric, photovol-
taic, biomass, biogas and biofuels, and took into account
social, economic, technological and environmental factors.
The authors identified biomass plants as the best alternative,
followed by wind power and solar thermo-electric. A greater
weight was given to power and the amount of carbon dioxide
emissions avoided per year. The authors used AHP-VIKOR
to determine possible renewable energy resource alternatives
to be installed at Banaras Hindu University, India [104].
They used AHP to weigh eight factors or criteria and then
applied VIKOR to measure the closeness of each alternative
to the ideal solution. They considered five alternatives,
namely, photovoltaic, concentrated solar power, wind tur-
bine, biomass and geothermal energy, all of which were se-
lected based on their geographical feasibility for onsite pow-
er generation. They identified wind turbine as the best
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 795
Table 8. The summary of previous work on TOPSIS and VIKOR method.
Authors Goal Field Method Description
Chakravorty and Ghosh,
2009 [22] Energy planning Energy/Power Distribution AHP
TOPSIS
To solve the network routing problem that occurs in
the power distribution system
Guo 2015 [100] Site selection Sustainability/Electric Vehicle
Charging Station fuzzy TOPSIS To determine the optimal electric vehicle charging
station in the sustainability perspective.
Wu et al., 2018 [101] Site selection Sustainability fuzzy ANP
fuzzy VIKOR
To determine the selection of large commercial rooftop
PV system in China using incomplete information
Sindhu et al., 2017
[102] Site selection Energy/ Solar Plant AHP
fuzzy TOPSIS
To select the best locations for the deployment of solar
farm in India
Wang et al., 2018 [103] Site selection Energy/Solar Power Plant
FAHP
DEA
TOPSIS
To provide a proper location for the deployment of
solar power plant using the proposed model
Cristóbal, 2011 [59] Method selection Energy/Renewable Energy AHP
VIKOR
Implementing the method in Spain to find the most
appropriate renewable energy investment
Wu et al., 2016 [106] Method selection Energy/Nuclear Power extended VIKOR Introducing an extended VIKOR method on supply
selection for the nuclear power industry.
Kumar and Samuel,
2017 [104] Method selection Energy AHP
VIKOR
To find the most suitable energy resources investment
project
Sattarpour et al., 2018
[105] Method selection Energy/Power Distribution Grid GA
TOPSIS
To provide a framework that can identify the sizing
and siting of DGs and RTUs in the power distribution
grid
Freeman and Chen,
2015 [107] Supplier selection Manufacturing
AHP
Entropy
TOPSIS
To assist the manufacturer on green supplier selection
to reduce the cost and environmental risk
Venkateswarlu and
Sarma, 2016 [108] Supplier selection Manufacturing SAW
VIKOR
To solve the problem that occurs in manufacturing by
selecting a suitable supplier
Awasthi et al., 2018
[109] Supplier selection Production fuzzy AHP
fuzzy VIKOR
To achieve sustainability in supplier selection by tak-
ing the sustainability risks into account
Xu et al., 2016 [110] Project assessment Sustainability/Power Grid
TOPSIS
Matter Element Exten-
sion
Implementing an evaluation model for the power grid
and renewable energy resources
You et al., 2017 [111] Project assessment Sustainability/Power Grid BWM
TOPSIS
To evaluate the operation of the performance of power
grid enterprise
Xu et al., 2017 [112] Project assessment Energy
Performances
DELPHI
VIKOR
To evaluate the service performances of EVS pro-
gramme conducted in Beijing
C.-N. Wang et al., 2021
[113] Project Assessment Renewable Energy
Performances
DEA window
fuzzy TOPSIS
Assessing the renewable energy capabilities on select-
ed countries
796 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
Table 9. The summary of all MCDM methods: advantages, disadvantages and our verdict.
Method Advantages Disadvantages Verdict
AHP
Easy to use and scalable
The importance of each element is
clarified due to its hierarchical structure
Convenient and straightforward
Interdependence amongst elements (criteria and
alternatives)
Not suitable for problems with too many criteria
and levels, which will lead to a lengthy process
May show some irregularities in ranking
Some important data may be lost due to the use of
additive aggregation
Increasing the number of alternatives/criteria may
lead to inconsistency in pairwise comparison
Suitable for most goals (plan, identify,
select and assess) with a small number
of criteria, can stand alone depending on
the criteria and their hierarchical rela-
tions and can be incorporated into GIS
for accurate location selection
ANP
Due to its network structure, independ-
ence is not required amongst elements
The priorities can be improved by feed-
back, hence improving prediction accu-
racy
Uncertainty may not be supported
Requires software and consumes much time due to
its complexity
ELECTRE
Take uncertainty and vagueness into
account
Considers both qualitative and quantita-
tive criteria
Complex processing
The strength and weakness of alternatives are not
directly identified
Difficult to understand and time consuming Suitable for goals requiring selection or
assessment, but given that both outrank-
ing methods have many variants, one
needs to choose a variant wisely. Some
variants can stand alone, and others may
perform better when hybridised. These
variants are often combined with pair-
wise comparison
PROMETHEE
Deals with both qualitative and quanti-
tative criteria
Criteria scores can be expressed in their
own unit
Easy to use and requires less input
Lacks a clear method for assigning weights. If
many criteria and options are available, then DMs
may face difficulties in evaluating the results
Whenever a new alternative is introduced, a rank
reversal problem may arise
PROMETHEE also has limited preference func-
tion and needs more preference functions or im-
provement to existing function for better result
TOPSIS
Has a simple process, hence facilitating
its use and programming
The number of steps remains the same
regardless of the number of attributes
Takes an infinite number of criteria and
attributes as inputs
Usage of Euclidean distance does not consider the
correlation amongst attributes
Difficult to weigh and maintain a consistent
judgement
Does not consider uncertainty in weightings
Mostly used for goals requiring selection
or assessment, but cannot stand alone
and must be combined with pairwise
comparison, fuzzy, GA or other methods
that can handle inconsistencies and
uncertainties
VIKOR
Has a simple process, hence facilitating
its use and programming
The number of steps remains the same
regardless of the number of attributes
Maximises the utility group and mini-
mises the regret group
Performance rating is quantified as a crisp value
Crisp data are inadequate for modelling a real-life
situation. DMs need to consider impre-
cise/ambiguous data
alternative, followed by photovoltaic energy. The DMs gave
the highest weights to investment cost, operation and
maintenance cost and environmental loss. In another study,
GA was combined with TOPSIS to develop a framework that
identifies the best way of integrating a demand response pro-
gramme for the optimal siting and sizing of distributed gen-
erations (DGs) and remote terminal units (RTUs) [105]. The
reactive power in DGs, along with the adaptive power factor,
contributed to minimising power loss and improving the
voltage profile.
An extended VIKOR method was introduced in [106] for
nuclear power industry supplier selection based on the cloud
model. The authors stated that the original VIKOR shows
some limitations when used under a linguistic environment
that may lead to inaccurate information or some information
loss. Therefore, they proposed the extended VIKOR to re-
duce such uncertainty. With its excellent handling of impre-
cise information, the cloud variable was used to define the
criteria, sub criteria and their uncertain weights. The authors
ensured the correctness and advantages of the extended VI-
KOR by using triangular fuzzy numbers and found that this
Multi-criteria Decision Making: A Systematic Review Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 797
approach can efficiently identify the best alternative. In
green supplier selection, the AHP-Entropy-TOPSIS model
was proposed to help manufacturers reduce environmental
risks and production costs and increase their competitiveness
[107]. One benefit of AHP-Entropy-TOPSIS is that this
model considers both quantitative and qualitative criteria. In
the first phase, the key dimensions and criteria for potential
suppliers are determined by conducting interviews with the
senior management representatives of companies. In the se-
cond phase, a two-part questionnaire survey is administered
to gather data for calculating subjective criteria weights via
AHP. In the third phase, supplier preference rankings are
obtained using TOPSIS by translating the entropy weight
calculations.
The VIKOR method was also widely used in supplier se-
lection, especially in the manufacturing industry, because
choosing an appropriate supplier can be considered a com-
plex problem, especially when dealing with fuzzy environ-
ments and limited information. Simple Additive Weighting
(SAW) was integrated with VIKOR to solve the supplier
selection problem in Anatapuram, India, aiming at reducing
waste and improving production quality [108]. The linguistic
variables for SAW were obtained from the ratings of qualita-
tive criteria using Saaty’s scale of pairwise comparison. The
involved criteria included performance, financial position,
management organisation just-in-time, and technical capabil-
ity. In the production field, fuzzy theory was integrated into
AHP-VIKOR to achieve a sustainable global supplier selec-
tion [109]. However, the authors lacked enough quantitative
information, thereby driving them to adopt fuzzy theory to
address this limitation. The process began by using fuzzy
AHP to weight the supplier criteria and ended with rating
supplier performance using fuzzy VIKOR based on certain
evaluation criteria. The selection involved two stages. The
first stage aimed for sustainability and global sourcing by
using economic, environmental, social, global risks and qual-
ity of relationship as criteria. The second stage extended the
evaluation criteria to cover the supplier sustainability risks
based on the environmental and social risks identified in the
first stage.
A study in Ningxia, China used the entropy weight meth-
od, an improved TOPSIS, a matter element extension and a
mathematical model to comprehensively evaluate regional
power grids and renewable energy power sources [110].
They stated that the traditional TOPSIS only managed to
rank the evaluation results and was unable to obtain the
evaluation classification. The authors in another study [111]
assessed the operation performance of a power grid enter-
prise by weighing their criteria using a hybrid of the best-
worst method (BWM) and TOPSIS. Specifically, they used
TOPSIS to rank the performance of the power grid based on
three criteria (economy, society and environmental factors)
and seven sub-criteria (overall labour productivity, asset-
liability ratio, return on equity, power supply reliability rate,
time of handling customer complaints, rate of purchasing
new energy power generation technologies and SF6 gas
emissions). In another study, given the wide usage of electric
vehicles across the globe, researchers in Beijing launched an
initiative aiming to promote the usage of these vehicles.
They employed a VIKOR-based approach to assess and im-
prove service performance in the Electric Vehicle Sharing
(EVS) programme [112]. They also used the Delphi method
as a survey technique to reach a consensus amongst the par-
ticipants. The DMs involved included an office worker, a
university student, a professor and a project manager from an
electric vehicle company. The authors combined several
methods, such as DEA window and fuzzy TOPSIS, to assess
the capability of 42 countries in producing renewable energy
[113]. At the first stage, the DEA window model was used to
choose a few potential countries. Afterwards, the variables
were well defined, given their potential effects on the DEA
process. Amongst the 42 countries identified, only 10 coun-
tries with the highest efficiency scores were selected. At the
second stage, fuzzy TOPSIS was implemented to rank the 10
selected countries based on five criteria, namely, availability
of resources, energy of security, technological infrastructure,
economic stability and social acceptance. Triangular fuzzy
numbers were used to determine the rating of each alterna-
tive and the weight of each criterion. Those countries with
the greatest capabilities were the US, Japan and Australia.
The reviewed studies on distance-based methods, includ-
ing TOPSIS and VIKOR, are summarised in Table 8 and
sorted according to their project goals and field of alterna-
tives. None of these studies used the original distance-based
method solely and always combined this method with fuzzy
combination or with other MCDM approaches.
4. RECOMMENDATIONS AND SUMMARY
Some issues encountered in the implementation of
MCDM methods and several recommendations for address-
ing these issues are presented in this section to generally
expose them for assisting our knowledge in choosing the
methods for our application. One major issue in pairwise
comparison is that adding a new criterion into the AHP mod-
el would require DMs to perform the calculation process all
over again [107, 114].
The entropy weight method can be implemented to over-
come this issue. AHP also requires expert judgment, which
often leads to its subjective nature, and DMs may face diffi-
culties in expressing their preference by using the defined
ratio scale [72]. Meanwhile, ANP utilises fuzzy numbers and
is therefore exposed to the subjective bias of DMs [86]. One
way to address this problem is to integrate ANP with any ag-
gregation method, such as TOPSIS, ELECTRE or MAUT.
ANP is also a time-consuming and exhaustive approach [80, 85]
that requires the use of additional third party software, such as
Super Decisions. The importance of each factor or criteria in
ANP cannot be measured quantitatively, but implementing
SWOT analysis can effectively solve this problem [77].
For outranking and distance-based methods, ELECTRI I
is generally preferred over TOPSIS because the former con-
siders both qualitative and quantitative criteria and the latter
does not consider the relative importance of the distance
from two reference points [38]. However, ELECTRE has a
limitation which requires the integration of the fuzzy method
to accelerate the process, where DMs can choose or discard
the unwanted alternatives with poor criteria [24]. Meanwhile,
PROMETHEE has limited preference function and needs
more preference functions or improvement to existing func-
tion for better result [21]. TOPSIS is also unable to deal with
insufficient information; if some criteria cannot be measured
798 Recent Advances in Electrical & Electronic Engineering, 2021, Vol. 14, No. 8 Azhar et al.
by crisp value but only with a fuzzy number, then the fuzzy
method should be adopted [100]. The problem with VIKOR
lies in the description of element (criteria/sub-criteria) in-
formation under linguistic environment, which can lead to
misinformation and poor accuracy. This problem can also be
solved by using the fuzzy method [106].
From the literature, we summarise the advantages and
disadvantages of all MCDM methods that have been dis-
cussed in this paper, together with our verdict in Table 9.
CONCLUSION
MCDM has been applied in various areas given its role in
solving real-world problems in energy, transportation, sus-
tainability, manufacturing and production. This approach
significantly reduces the time consumed by DMs in complex
DM processes. MCDM approaches can be integrated not
only with one another but also with other methods, such as
fuzzy, grey sets, machine learning and GIS, which have been
explored throughout this paper. A detailed survey on well-
known MCDM methods, such as AHP, ANP, ELECTRE,
PROMETHEE, TOPSIS and VIKOR, is presented in this
paper. The implementation of these methods is crucial in
solving complex problems that cannot be addressed via
straightforward human thinking. However, some methods
are incapable of handling problems with insufficient infor-
mation. Thus, some researchers have integrated MCDM into
other methods. Another ongoing issue is that the application
of MCDM requires much time and expensive calculations.
Future reviews should explore the usage of other MCDM
methods, such as MAUT, MAVT, GP and COPRAS, in dif-
ferent fields.
In addition, investigation/review can be made on modifi-
cations of the MCDM methods (modified AHP, TOPSIS,
ELECTRE, and etc.) in the literature for uncertain sets (such
as fuzzy set, rough set, soft set, etc.) in future studies.
CONSENT FOR PUBLICATION
Not applicable.
STANDARD OF REPORTING
PRISMA guidelines were followed in this study.
FUNDING
This work was supported in part by UNITEN R & D Sdn
Bhd through TNB Seed Fund under Grant UTD-RD-20-03.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest re-
garding the publication of this paper.
ACKNOWLEDGEMENTS
Declared none.
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