Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3726  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

A Multi-Criteria Decision Making Approach for 

Enhancing University Accreditation Process 
 

Nezha Benmoussa 

Signals, Distributed Systems and Artificial Intelligence 

Laboratory, ENSET Mohammedia, University of Hassan II, 

Mohammedia, Morocco 

nbnezhabenmoussa@gmail.com 

Abir Elyamami 

Signals, Distributed Systems and Artificial Intelligence 

Laboratory, ENSET Mohammedia, University of Hassan II, 

Mohammedia, Morocco 

abir.elyamami@gmail.com 

Khalifa Mansouri  

Signals, Distributed Systems and 

Artificial Intelligence Laboratory, 

ENSET Mohammedia, University of 

Hassan II, Mohammedia, Morocco 

khmansouri@gmail.com  

Mohammed Qbadou  

Signals, Distributed Systems and 

Artificial Intelligence Laboratory, 

ENSET Mohammedia, University of 

Hassan II, Mohammedia, Morocco 

qbmedn7@gmail.com  

Elhoussein Illoussamen 

Signals, Distributed Systems and 

Artificial Intelligence Laboratory, 

ENSET Mohammedia, University of 

Hassan II, Mohammedia, Morocco 

Illous@hotmail.com 
 

 

Abstract— This paper is an attempt to provide an accreditation 
training process model by the criteria established by the National 

Agency for Evaluation and Quality Assurance of Higher 

Education and Training. The aim is to minimize rejection returns 

or revisions of the training record. The main feature of our 

contribution is the use of multi-criteria decision making (MCDM) 

approaches for calculating the suitability of proposed courses. 

Therefore, our contribution will is concretized by the analysis of 

various decision support multi-criteria methods, the modeling of 

the general accreditation process of university courses, the 

development of a risk management matrix concerning the launch 

of new courses and the application of TOPSIS (technique for 

order of preference by similarity to ideal solution) on a sample of 

courses according to internal and external criteria, collected 

during interviews in Moroccan universities. Result analysis shows 

that the proposed model allows a better prioritization of training 

and thus avoids the abrupt closure of courses because of lack of 

material or human resources. 

Keywords- decision support; MCDM; accreditation; risk 
management matrix; TOPSIS 

I. INTRODUCTION  

Decision-making generates very important industrial and 
economic issues affecting management and competitiveness of 
organizations. Prioritizing and optimizing all actions are two 
key factors of effective decision-making. Universities face the 
challenge of resources and investment optimization to avoid 
any negative impact on the management of training projects 
and their performance. They are always looking for innovation 
so that they may be able to meet the needs of the market and to 
encourage scientific research. All interviewed managers 
assume that taking an effective decision in advance, requires a 
preliminary study of any educational proposal. Multi-criteria 
decision making (MCDM) methods, are increasingly used in 
various fields like natural resource management, environment 

and spatial planning, making possible to rely on science while 
taking managerial decisions and to drive decision-making 
processes in organized systems [1]. Whether strategic, global, 
operational or local, decision making is generally made to 
manage organizations consistently: quantitatively (number of 
products or services offered), and qualitatively (development of 
standards, establishment of a charter). Therefore, making a 
decision requires different alternatives that must be evaluated 
according to one or more criteria in order to determine the 
optimum [2]. These alternatives and indicators help 
enormously in decision-making and are a great contribution to 
the evolution of future steps to take. 

Decision support is a scientific approach to decision-
making problems that arise in any socio-economic context in 
which the two main factors are the decision-maker who 
governs the decision-making process, and the responsible of 
study who intervenes at least on 1 of the 3 important levels, 
namely the modeling of the decision problem, the design or 
adaptation of a procedure for exploiting the model and the 
elaboration of a prescription from the solution(s) [3]. Formerly, 
decision support was designed to find the solution to a given 
problem. Today, it decently offers answers in the form of 
recommendations to decision-makers of a decision-making 
process and allows them to make better choices [4]. 
Operational research (OR) offers a variety of decision support 
tools and the most complex decisions and resource 
optimizations are possible through a number of algorithmic 
approaches that iteratively build a solution, descent heuristics 
that seek a global optimum from a given solution and meta-
heuristics that break down objectives to ease decision-making. 
These purely algorithmic resolution methods in the decision-
making domain resolve in time, efficiently and quickly, to a 
solution or choice [5]. Indeed, OR is a discipline of scientific 
methods that help the making of a decision. It refers to notions 

Corresponding author: N. Benmoussa  



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3727  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

that map our contemporary semantic and legal territory to the 
image of big data, e-reputation, or predictive algorithms [6]. 
Multicriteria decision support is a major area of study of OR 
involving several schools of thought, mainly American [7]. 
These are mathematical methods to choose the best solution or 
the optimal solution among a whole set of solutions. 

In this paper, we contribute by analyzing various MCDM 
methods, modeling the general process of accrediting academic 
training and developing a risk management matrix for the 
launch of new courses and the application of TOPSIS on a 
sample of courses according to internal and external criteria, 
collected during interviews for an effective decision-making 
process within Moroccan universities.  

II. MCDM METHODS 

Multicriteria decision aid (DMA) was created in the 1970s. 
It has aroused interest through its innovative approach starting 
with single-criteria analysis followed by the weighting of 
criteria and their aggregation procedures varied in decision 
problems [8]. Most conventional MCDM methods use 
parameters derived from the decision maker's preferences. 
These parameters are often used for weight calculation, 
quantifying the importance of each criterion in the multicriteria 
decision process. This domain is broken down into two 
subdomains: 

• MADM (multi-attribute decision making) for selecting the 
best alternative in a predetermined set of alternatives. 

• MODM (multi-objective decision making) concerning the 
selection of the best action in a continuous or discrete 
decision space. Multi-objective optimization is a branch of 
MODM [9]. 

Authors in [10] specify that in a multi-criteria decision 
support process, the main objective is not to find a solution, but 
to build or create a tool considered as useful in the decision-
making process. Since then, the MCDM methods are more and 
more used including MAXMIN, MAXMAX, SAW, AHP, 
TOPSIS, SMART and ELECTRE [11] in order to make a 
choice, to classify or to sort out for an effective decision 
making. The MCDM's progression goes through 6 steps as 
shown in Figure 1. 

 

 

Fig. 1.  General steps of MCDM methods 

The test steps are important because they give an accurate 
assessment of the indicators. If the indicator’s quality isn’t 

conformed to the goals, it must be redefined and the test must 
be recommenced. Below we present some decision support 
methods which will be the object of a concise description 
specifying their functioning and their limits. TOPSIS will be 
more detailed since it constitutes the implementation tool of the 
data of our case study “accreditation and training 
management”. The analyzed methods’ advantages and 
limitations are shown in Table I. 

A. AHP (Hierarchical Process Analysis)  

AHP is a semi-quantitative method that has been developed 
in [12] and its computer version “Expert Choice” software was 
introduced in the US in 1985. It is based on the comparison of 
pairs of options and criteria by structuring in a logical 
coherence: 

• Classes, criteria and hierarchical weights. 

• Sub-criteria and ranks by priority. 

This involves making pairs of elements of each hierarchical 
level with an element of the higher hierarchical level. This step 
makes possible to build comparison matrices. The values of 
these matrices are obtained by the transformation of judgments 
into numerical values according to the Saaty scale [13], while 
respecting the principle of reciprocity: 

𝑃𝑐(𝐸𝐴, 𝐸𝐵) =
1

𝑃𝑐(𝐸𝐵,𝐸𝐴)
    (1) 

B. SMART (Simple Multiple Attribute Rating Technique) 

SMART is similar to AHP, it has been developed since 
1971 as a hierarchical structure created to assist in defining a 
problem and in organizing criteria. The difference between a 
value tree and a hierarchy in AHP is that the value tree has a 
true tree structure, allowing one attribute or sub-criterion to be 
connected to a higher level criterion. Its main steps are: 

• Put the criteria in decreasing order of importance. 

• Determine the weight of each criterion. 

• Normalize the relative importance coefficients between 0 
and 1: sum the importance coefficients and divide each 
weight by this sum. 

• Measure the location of each action on each criterion (uj 
(αi)). Evaluations actions are on a scale ranging from 0 
(plausible minimum) to 100 (plausible maximum). 

• Determine the value of each share based on the following 
weighted sum: 

𝑈(𝑎𝑖) =  ∑ 𝜋𝑗𝑢𝑗(𝑎𝑖)
𝑛
𝑗=1 , i=1, 2… m  (2) 

• Classify actions in decreasing order of U(αi). 

C. TOPSIS 

TOPSIS was presented in [14] and developed later in [15, 
16]. It is worth noting that it corresponds to the Hellwig 
taxonomic method of ordering objects [17]. The main 
advantages of this method are: it is a simple, rational, 
comprehensible concept, and it has intuitive and clear logic that 
represents the rationale of human choice. In this method, two 
alternatives are hypothesized: the ideal solution that has the 



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3728  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

best solution for all attributes and the negative ideal solution 
for the one which has the worst attribute values. TOPSIS 
method performs prioritization of alternatives based on their 
geometric distance from the positive-ideal and negative-ideal 
solution. It reduces the need of pair comparisons and the 
limitation of capacity may not significantly dominate the 
process. Therefore, it would be appropriate for cases with a 
large number of criteria and alternatives, especially when 
objective or quantitative data are determined [18]. TOPSIS 
breaks down the decision into different stages: 

1) Formation of Decision Matrix 

Criterion outcomes of decision alternatives are collected in 
a decision matrix. The matrix rows represent decision 
alternatives, with matrix columns representing criteria. A value 
found at the intersection of row and column in the matrix 
represents a criterion outcome: a measured or predicted 
performance of a decision alternative on a criterion. 

 C1CjCn 

X=

𝐴1
⋮

𝐴2
⋮

𝐴𝑚

[

 𝑥11
⋮

⋯ … 𝑥1𝑗
⋮

… 𝑥1𝑛
⋮

𝑥𝑖1
⋮
… … 𝑥𝑖𝑗

⋮

… 𝑥𝑖𝑛
⋮

𝑥𝑚1… … 𝑥𝑚𝑗 … … 𝑥𝑚𝑛

]

𝑚×𝑛

  (3) 

where, xij is the performance rating of alternative i with respect 
to criterion j, Ai is ith alternative and Cj is the jth criterion. 

2) Formation of Weight Matrix 

Different importance weights to various criteria may be 
awarded by the decision maker independently or by entropy 
method. These importance weights form the weight matrix: 

W=[W1 …..Wj …..Wn]     (4) 

3) Normalization of Performance Rating  

Units and dimensions of performance ratings under criteria 
differ. For comparison, these performance ratings are converted 
into dimensionless units by normalization using the following 
equations: 

( )
ij

ij

ij
i

x
x

max x
=      (5) 

for benefit criteria j and 

( )ij
i

ij

ij

min x
x

x
=      (6) 

for non-benefit criteria j  

Finally, the normalized decision matrix is formed: 

=X

A1
⋮

Ai
⋮

Am [
 
 
 
x̅11

⋮
⋯ … x̅1j

⋮

… x̅1n
⋮

x̅i1
⋮
… … x̅ij

⋮

… x̅in
⋮

x̅m1 x̅mj x̅mn]
 
 
 

m×n

  (7) 

4) Determination of the Positive Ideal and Negative Ideal 

Solution 

A+  = (ai1
+ ,ai2

+ , … … … … … … . . , aim
+ ), 

aij
+= max

1≤i≤m
(aij), j=1, 2…, n    (8) 

A−  = (ai1
− ,ai2

− , … … … … … … . . , aim
− ), 

aij
−= min

1≤i≤m
(aij), j=1, 2…., n    (9) 

5) Calculation of the Separation Measures 

Using the n-dimensional Euclidean distance the separation 
of each alternative from the positive ideal solution is given as: 

𝐷𝑖
+=√∑ 𝑊𝑗(𝑎𝑖𝑗

+ − 𝑎𝑖𝑗)
2𝑛

𝑗=1    (10) 

Similarly, the separation from the negative ideal solution is 
given as: 

𝐷𝑖
−=√∑ 𝑊𝑗(𝑎𝑖𝑗

− − 𝑎𝑖𝑗)
2𝑛

𝑗=1    (11) 

6) Step6: Calculation of the Ratio 

For each alternative, calculate the ratio Ri as: 

𝑅𝑖 =  
𝐷𝑖

−

𝐷𝑖
+ + 𝐷𝑖

− i = 1, 2. . . . . . m    (12) 

7) Rank Alternatives in Increasing Order According to the 

Ratio Value of Ri 

D. ELECTRE I & II  

ELECTRE I (Elimination and Choice Translating REality) 
was developed in 1968 and ELECTRE II in 1971 [19]. Both 
versions are based on the notions of concordance and 
discordance. ELECTRE is a non-compensatory method of 
multicriteria decision support. In reality, the decision maker is 
often undecided, his preferences evolve because the decision is 
the result of a process of micro decisions. The optimum can 
only be achieved if three conditions are met: 

• The different strategies (projects) proposed to the decision 
maker are distinct. 

• Strategies stability over time is present. 

• Comparability is transitive.  

E. ELECTRE III 

Electre III is based on a fuzzy logic and a constructive 
approach which classifies actions. It favors [20]: 

• Dialogue between the different factors in the decision-
making process. 

• Weighting of criteria by factors expressing preferences on 
resource management strategies. 

• Consideration of uncertainty in the evaluation of actions by 
pseudo-criteria. 

F. ELECTRE IV 

This method assumes that all pseudo-criteria are of equal 
importance. It involves two outranking relations like 
ELECTRE II but only one set of veto thresholds and the notion 
of concordance is translated by a notion of majority of criteria 
in the absence of any weighting. 



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3729  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

TABLE I.  MCDM METHODS: ADVANTAGES AND LIMITATIONS 

AHP 

Popular method that has been subjected to criticism 

regarding the explosion of the number of pairwise 

comparisons in case of a complex problem, the reversal of 
rank (order of priority of the actions) in case of addition or 

deletion and the introduction of biases by the association of 

a numerical scale on the semantic scale which is restrictive. 
Currently, AHP is subject to several extensions such as the 

consideration of uncertainty (stochastic AHP) and blur 

(fuzzy AHP) in the expression of judgments. 

SMART 

Similar to AHP and easy to exploit but requires a priori 

articulation of preferences, and evaluation of actions on a 

single scale (cardinal scale). It is compensatory and has 
been developed in Criterium Decision Plus 3.0 and Decide 

Right for automatic management. 

TOPSIS 

Easier method to apply and responsive to the decision 
maker's wishes. However, the attributes must be cardinal in 

nature, preferences are fixed a priori. On the other hand, if 

all the actions are bad, the method proposes the best of 

these bad actions. 

ELECTRE I  

It formalizes well the process of human reasoning, but has 

the disadvantage of using quantization weights of the 

importance of different criteria. Despite the contribution of 
this method, the decision maker still faces the difficult task 

of providing quantization weights. 

ELECTRE II 
It replaces the classic upgrade relationship with two new 
relationships, namely strong upgrade and weak upgrade. 

ELECTRE III 

It introduces the notion of pseudo-criteria that replace the 

classical criteria. The pseudo-criteria are modeled by 

functions whose expression is close to the membership 
functions known in the field of fuzzy logic. 

ELECTRE IV 

Is distinguished by its ability to dispense with the weights 

associated with each criterion. However, this benefit is 
tempered by the need to determine a “credibility degree” 

associated with each outranking relationship used. 

GRA 

Uses a specific concept of information. It defines situations 
with no information as black, and those with perfect 

information as white. Neither of these idealized situations 

ever occurs in real world problems. Situations between 
these extremes are described as being grey, hazy or fuzzy. 

PROMETHEE 

GAIA 

 

Unlike the outranking relation constructed by the 

ELECTRE method, which is purely binary, the relation 

constructed by PROMETHEE is a valued upclass 
relationship: one action outclasses another with a 

numerical preference intensity. In 1989, GAIA provided a 

descriptive complement to PROMETHEE rankings. Using 
a graphical representation of the multicriteria problem, the 

decision maker can easily understand which choices are 

possible and which trade-offs are required to make a good 
decision. PROMETHEE-GAIA require less 

parameterization while remaining as efficient. It makes 

possible to stay closer to the real decision problem, to 
better describe it and to carry out sensitivity analyses. 

 

G. Gray Relational Analysis (GRA) 

Also called Deng's gray incidence analysis model [21], 
GRA uses a specific concept of information. It defines 
situations with no information as black, and those with perfect 
information as white. However, neither of these idealized 
situations ever occurs in real world problems. In fact, situations 
between these extremes are described as being gray or fuzzy 
[22]. The scope of the gray system involves agriculture, 
ecology, economics, meteorology, medicine, history, 
geography, industry, earthquake, geology, hydrology, 
irrigation, strategy, military affairs, sport, traffic, management, 
materials science, environment, biological protection, judicial 
system. 

H. PROMETHEE 

Preference ranking organization method for enrichment of 
evaluations (PROMETHEE) is a part of the family of upgrade 
methods that allow two particular mathematical treatments: 
partial storage (PROMETHEE I) and complete storage 
(PROMETHEE II). With their descriptive complement 
geometrical analysis for interactive aid they are better known 
under the names PROMETHEE and GAIA1 [23]. They are 
multi-criteria decision-support methods that belong to the 
family of methods of outreach initiated by the ELECTRE 
methods. PROMETHEE and GAIA methods offer a 
prescriptive and descriptive approach to the analysis of discrete 
multicriteria problems covering several areas. In fact, 217 
scientific articles from 100 journals mention their fields of 
application on environmental management, hydrology and 
water management, commercial and financial management, 
chemistry, logistics and transport, manufacturing and assembly, 
energy management and other topics such as medicine, 
agriculture, education, design, government, and sport [24, 25].  

III. RESEARCH METHODOLOGY 

The proposed approach concerns the field of higher 
education in general and the Moroccan universities in 
particular. It meets the obligation to set up indicators before 
launching a new course. Indeed, the objective of this study is to 
contribute to the optimization of resources and especially the 
evaluation of existing training as well as the decision-making 
concerning those to be accredited. For the understanding of the 
accreditation process, we modeled the demands management 
standard and developed the corresponding risk management 
matrix. In addition, we studied university specifications and 
conducted interviews with university officials on the basis of 
internal and external criteria that are essential for the training. 
These data will be presented and commented. For the 
evaluation of our proposal, we applied TOPSIS on a course 
sample. 

A. Model of Accreditation of Innovative University Courses 

The compliance with the standards and criteria stipulated 
by the National Agency for Evaluation and Insurance Quality 
of Higher Education and Scientific Research (ANEAQ) is 
essential for the acceptance of the training proposed by any 
institution. The following model presents the general process 
for the processing of accreditation requests for training and the 
main conditions to be taken into consideration in order to avoid 
the return of refusals or major revisions: at least 1 teacher per 
higher grade, the appropriate hourly volume in theory and 
practice as well as internships and partnerships to prepare 
profiles for the professional world as shown in Figure 2. 

B. Risk Management Matrix 

The interviewees unanimously expressed the usefulness of 
the designed risk management matrix which specifies not only 
the main risks to be considered, but also the responsibility and 
actions to be taken to make the decision effective. They have 
completed it and have judged that these risks are to be 
minimized or even avoided and must be taken into 
consideration before any training proposal (Table II). 

 



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3730  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

 

Fig. 2.  Processing process for training accreditation applications 

TABLE II.  RISK MANAGEMENT MATRIX 

Risk 
Probability 

rating 

Severity 

rating 

Priority 

rating 
Responsibility 

Action to be 

taken 

Lack of 
classrooms 

3/5 4/5 12/25 University 
Construction 
Enlargement 

Lack of 

human 

resources 

4/5 4/5 16/25 Establishment 

Continuing 

Education 

Session 

Lack of 

equipment 
4/5 5/5 20/25 

Establishment/ 

University 
Acquisition 

 

The risk management matrix in Table II clearly shows that 
teaching resources and human resources are the most important 
according to their respective ratings of 20/25 and 16/25, which 
is explained by the risk of lack of rooms that can be solved by 
adequate automatized planning or an expansion of the 
establishment. Thus, the managers must establish a policy of 
continuous training of human resources permanently according 
to the evolution of the market and an adequate budgetary 
strategy relating to the educational equipment necessary for 
each department. To make the study relevant, we have defined, 
in addition to these indicators, internal and external criteria 
based on program specifications and interviewee responses. 

C. Internal and External Criteria 

Table III provides the main internal criteria of Moroccan 
university courses, the main objective of the departments is the 
adequacy of the proposals with the university strategy which 
aims to reach 100% in terms of innovation and development in 
order to meet the expectations of the market and encourage 
scientific research. Table III illustrates the results of the 
interviews based on the specifications and the actual estimate 
for each of the criteria according to the specifications. We were 
able to determine 7 internal and 3 external criteria. The internal 
ones are part of the school's strategy and are the key to the 
success of the existing training and future ones. Externally, 
these are the conditions to be respected for alignment with the 
standards put in place concerning requests for accreditation of 
new training. These criteria will allow university officials to 
detect, qualitatively and quantitatively, the defects and report 
on the improvement of the situation and good planning. In 
addition to the ratings in the risk management matrix and 

standards in the accreditation processing model, the vision will 
be clear in identifying priority areas for improvement to avoid 
inefficient decision-making. The external criteria are shown in 
Table IV and are complementing the internal ones in order to 
prepare competent profiles in adequacy with the socio-
economic status and respect for the ANEAQ standards. They 
concern especially the rank of human resources, the hourly 
amount and the content which must be theoretical and practical. 
Table IV shows that the partnership is essential and must be 
developed for all courses in order to gain knowledge of the 
market and easy integration in active life and internships. It is 
therefore necessary to maximize the agreements and 
partnerships with companies and administrations. Internal and 
external criteria, partnership and internships considerably 
influence the decision to be taken and constitute the key to 
evaluate existing training cards and launching new training. 
They will be implemented via TOPSIS in order to conclude 
with recommendations of interest for our universities. 

TABLE III.  MAIN INTERNAL CRITERIA 

Criteria Motivation Human resources Technical resources 

course innovation 
market 

needs 
team experience 

class- 

rooms 

work- 

shops 
equipment 

BDCC 95% 95% 50% 100% 65% 30% 40% 

GLSID 90% 80% 65% 90% 65% 40% 45% 

MLI 85% 80% 65% 90% 65% 35% 40% 

GMSI 75% 85% 70% 90% 70% 50% 45% 

SID 70% 80% 75% 75% 65% 40% 40% 

GECSI 80% 85% 65% 90% 70% 45% 45% 

GMASI 75% 80% 70% 90% 70% 50% 45% 

TABLE IV.  EXTERNAL CRITERIA 

Alignment with standards 
Partnership Traineeship 

Grade VH Content 

2 PES 100% T/P Yes Yes 

3 PA 100% T/P Yes Yes 

2 PA 100% T/P Yes Yes 

1 PA 100% T/P Not yet Yes 

4 PA 100% T/P Not yet Yes 

3 PA 100% T/P Not yet Yes 

1 PA 100% T/P Yes Yes 

1 PA 100% T/P Not yet Yes 

 

D. TOPSIS Implementation 

After the identification of the alternatives and criteria, the 
results of our interviews will be implemented under the 
MCDM TOPSIS in order to identify the importance 
coefficients and prioritize the best decision. We will then 
calculate the weighted scores for each of the formations 
according to the standardized criteria and values in order to 
prioritize and optimize the different formations.  

1) Formation of Decision Matrix  

The choice of alternatives and criteria is the most important 
step in decision making. Indeed, selecting the key indicators is 
a basis that will allow university officials to prioritize and 
optimize actions for better management. The alternatives and 
criteria codes are presented in Figure 3. 

2) Formation of Weight Matrix 

In Table V we see the selected alternatives and the scoring 



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3731  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

of each alternative on different criteria. The dataset is used as 
decision matrix and then the normalized decision matrix is 
calculated (Table VI). 

 

 

Fig. 3.  Codification of internal and external criteria 

TABLE V.  WEIGHT MATRIX 

aij C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 

A1 95 95 50 90 65 30 40 43 1 1 

A2 95 100 65 100 80 80 45 43 1 1 

A3 85 80 65 90 65 35 40 38 1 1 

A4 75 85 70 90 70 50 45 38 0 1 

A5 70 80 75 75 65 40 40 43 0 1 

A6 80 85 65 80 70 45 45 26 0 1 

A7 75 80 70 90 70 50 45 38 1 1 
 

3) Determination of Positive and Negative Ideal Solutions 

Positive and negative ideal solutions, 𝐴+and 𝐴− are defined 
according to the normalized decision matrix (Table VII). 

4) Calculation of the Separation Measures 

For each competitive alternative the separation distance is 
calculated in Table VIII. 

5) Ratio Calculation 

The relative closeness of each location to TOPSIS ideal 
solution is measured in Table IX. 

6) Classify Actions 

The alternatives are ranked in decreasing order (Table X). 

TABLE VI.  PERFORMANCE RATING 

Rij  

A1 0.43 0.41 0.29 0.39 0.35 0.23 0.35 0.42 0.50 0.38 

A2 0.43 0.44 0.37 0.43 0.44 0.61 0.40 0.42 0.50 0.38 

A3 0.39 0.35 0.37 0.39 0.35 0.27 0.35 0.37 0.50 0.38 

A4 0.34 0.37 0.40 0.39 0.38 0.38 0.40 0.37 0.00 0.38 

A5 0.32 0.35 0.43 0.32 0.35 0.31 0.35 0.42 0.00 0.38 

A6 0.37 0.37 0.37 0.34 0.38 0.34 0.40 0.25 0.00 0.38 

A7 0.34 0.35 0.40 0.39 0.38 0.38 0.40 0.37 0.50 0.38 

TABLE VII.  POSITIVE AND NEGATIVE IDEAL SOLUTIONS 

vmax A+ 0.43 0.44 0.43 0.43 0.44 0.61 0.40 0.42 0.50 0.38 

vmin A- 0.32 0.35 0.29 0.32 0.35 0.23 0.35 0.25 0.00 0.38 

TABLE VIII.  SEPARATION MEASURES 

Course code Smin Smax 

A1 0.546676 0.420843 

A2 0.686478 0.057166 

A3 0.53037 0.379255 

A4 0.240516 0.568416 

A5 0.231595 0.61971 

A6 0.161789 0.609396 

A7 0.554413 0.276441 

TABLE IX.  RATIO VALUES 

Course code Coefficients Ranking 

A1 0.923127 1 

A2 0.667281 2 

A3 0.583065 3 

A4 0.565028 4 

A5 0.297325 5 

A6 0.272047 6 

A7 0.209792 7 

TABLE X.  RANKING VALUES 

Course Coefficients Ranking 

GLSID 0.923127 1 

GMASI 0.667281 2 

MLI 0.583065 3 

BDCC 0.565028 4 

GMSI 0.297325 5 

SID 0.272047 6 

GECSI 0.209792 7 
 

IV. DISCUSSION 

Compensatory methods such as TOPSIS allow trade-offs 
between criteria, where a poor result in one criterion can be 
negated by a good result in another. It provides a more realistic 
form of modeling than non-compensatory methods. Using 
TOPSIS, the collected results respond to our problematic, 
which aims to innovate in the field of university training, 
whether initial or continuous, and above all to prioritize and 
optimize actions in order to remedy the difficulties encountered 
before, during and after the launch of a new course. Figure 4 
illustrates the degree of prioritization of different courses 
according to the studied criteria. 

 

 

Fig. 4.  Prioritization 

The result of the proposed method shows that the first 4 
courses are favored by several factors that can be summarized 
in: 

9
2
.3
1
%

6
6
.7
3
%

5
8
.3
1
%

5
6
.5
0
%

2
9
.7
3
%

2
7
.2
0
%

2
0
.9
8
%

GLSID GMASI MLI BDC C GMSI SID GE C SI



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3732  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

• Relevant course modules 

• Interesting potential of human resources 

• Strong demand for these specialties on the market 

However, for all courses, we must focus on the material and 
teacher’s specialties constraints. In addition, some modules 
may be enriched, deleted or replaced. The results show that the 
engineering, industry and big data rank first in relation to 
others because all criteria are fulfilled. This explains why, 
before launching a new course, we have to focus on the 
indicators studied and evaluate the limits of the human and 
material resources which are at the origin of the success of any 
course. In addition, we must prioritize these courses because 
they are innovative and meet current and immediate market 
expectations. Admittedly, they require more teaching resources 
and considerable experience in the field, but the situation can 
be improved by setting up performance tools such as balanced 
scorecards. Our method allowed the evaluation of possible 
alternative solutions for the continuous improvement of the 
performance of the studied formations. The proposed 
framework can help universities identify the strengths and 
weaknesses of their human and material resources. It also 
makes it easier for decision-makers to plan future strategies to 
develop educational courses and identify the best practices for 
each course while respecting current strategies, the job market, 
and the technological evolution. 

V. CONCLUSION 

The main goal of this study was to provide a decision-
making tool for effective management of the initial and 
continuing training map. We focused on the case of multi-
criteria decision-making in the system of applications for 
accreditation of innovative training in order to prioritize 
decisions and the incessant changes in the labor market. It 
should be noted that the interaction between the different 
stakeholders (decision makers, department heads and teachers) 
is always present. In this context, we opted for MCDM 
methods in general and TOPSIS in particular, which uses 
precise parameters to calculate the performance for each 
studied element. Using this tool, we were able to quantify the 
internal and external criteria identified from specifications and 
interviews. These indicators were determined and can be 
expanded in the future according to the academic needs and 
standards. They allowed us to to enable decision-makers to 
target the best alternative and also to correct those that are 
interesting but have lower results. In conclusion, the multi-
criterion aspect and the proposed approach combining the 
analysis of internal/external criteria via the TOPSIS tool, made 
possible to select and prioritize the actions to be taken for an 
effective decision. These coefficients will help university 
officials in determining the value of different alternatives in 
order to focus human and material resources on the 
emergencies to be addressed and the innovative trainings to be 
planned. When it comes to future work, this same approach can 
be completed by a calculation of weights of each of the criteria 
announced or taken over with Fuzzy TOPSIS or PROMETHEE 
GAIA for more details, especially the management of 
uncertainties and sensitivity of situations through a prescriptive 

and descriptive approach, and scorecards for helping in 
decision-making. 

REFERENCES 

[1] M. Diaby, F. Valognes, A. Clement-Demange, “Utilisation d’une 
methode multicritere d’aide a la decision pour le choix des clones 
d’hevea a planter en Afrique”, Biotechnologie, Agronomie, Societe et 
Environnement, Vol. 14, No. 2, pp. 299-309, 2010 (in French) 

[2] D. Vanderpooten, Aide Multicritere a la Decision Concepts, Methodes et 
Perspectives, ENS Cachan, 2008 (in French) 

[3] S. B. Mena, “Introduction aux methodes multicriteres d’aide a la 
decision”, Biotechnologie, Agronomie, Societe et Environnement, Vol. 
4, No. 2, pp. 83-93, 2000 (in French) 

[4] J. Ninin, L. Mazeau, “La recherche operationnelle: De quelques enjeux 
juridiques des mecanismes d’aide a la decision”, Lex Electronica, Vol. 
22, pp. 57-79, 2017 (in French) 

[5] D. C. Porumbel, “Algorithmes Heuristiques et Techniques 
d’Apprentissage - Applications au Probleme de Coloration de Graphe”, 
PhD Thesis,  Université d'Angers, 2009 (in French) 

[6] B. Roy, Regard Historique sur la Place de la Recherche Operationnelle 
et de l’Aide a la Decision en France, Universite Paris-Dauphine, 2006 
(in French) 

[7] T. L. Saaty, “Decision making with the analytic hierarchy process”, 
International Journal of Services Sciences, Vol. 1, No. 1, pp. 83-98, 
2008 

[8] B. Roy, H. Aissi, Robustesse en Aide Multicritere a la Decision, 
Universite Paris-Dauphine 2008 (in French) 

[9] A. Appriou, “Methodologie de la gestion intelligente des senseurs”, 
Traitement du Signal, Vol. 22, No. 4, pp. 305-306, 2005 (in French) 

[10] B. Roy, D. Bouyssou, Aide Multicritere a la Decision: Methodes et Cas , 
Economica, 1993 (in French) 

[11] M. Hanine, O. Boutkhoum, A. Tikniouine, T. Agouti, “Application of an 
integrated multi-criteria decision making AHP-TOPSIS methodology for 
ETL software selection”, Springerplus, Vol. 5, pp. 1-17, 2016 

[12] T. L. Saaty, L. G. Vargas, Models, Methods, Concepts & Applications 
of the Analytic Hierarchy Process, Springer, 2001 

[13] F. Tscheikner-Gratl, P. Egger, W. Rauch, M. Kleidorfer, “Comparison 
of Multi-Criteria Decision Support Methods for Integrated 
Rehabilitation Prioritization”, Water, Vol. 9, No. 2, pp. 1-28, 2017 

[14] D. Ozturk, F. Batuk, “Technique for order preference by similarity to 
ideal solution (topsis) for spatial decision problems”, ISPRS 4th 
International Workshop, Trento, Italy, March 2-4, 2011 

[15] A. Mardani, A. Jusoh, K. Nor, Z. Khalifah, N. Zakwan, Alireza, 
“Multiple criteria decision-making techniques and their applications”, 
Economic Research-Ekonomska Istrazivanja, Vol. 28, No. 1, pp. 516-
571, 2015 

[16]  T. Jolanta, Z. Edmundas, T. Zenonas, P. Vainiunas, “Multi-criteria 
complex for profitability analysis of construction projects”, Economics 
and Management, Vol. 16, pp. 969-973, 2011 

[17] Z. Edmundas, M. Abbas, T. Zenonas, J. Ahmad, N. Khalil, 
“Development of TOPSIS method to solve complicated decision-making 
problems”, International Journal of Information Technology & Decision 
Making, Vol. 15, No. 3, pp. 645-682, 2016  

[18] G. O. Odu, O. E. Charles-Owaba, “Review of Multi-criteria 
Optimization Methods – Theory and Applications”, IOSR Journal of 
Engineering, Vol. 3, No. 10, pp. 1-14, 2013 

[19] D. Ayadi, Optimisation Multicritere de la Fiabilite: Application du 
Modele de Goal Programming Avec les Fonctions de Satisfactions dans 
l’Industrie de Traitement de Gaz, PhD Thesis, Universite d’Angers, 
2010 (in French) 

[20] K. Solecka, “ELECTRE III method in assessment of variants of 
integrated urban public transport system in Cracow”, Transport 
Problems: an International Scientific Journal, Vol. 9, No. 4, pp. 83-96, 
2014 



Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3726-3733 3733  
  

www.etasr.com Benmoussa et al.: A Multi-Criteria Decision Making Approach for Enhancing University Accreditation … DO NOT ALTER HEADER & FOOTER. THEY WILL BE COMPLETED DURING EDITING  

 

[21] L. Sifeng, C. Hua, C. Ying, Y. Yingjie, “Advance in grey incidence 
analysis modelling”, 2011 IEEE International Conference on Systems, 
Man, and Cybernetics, Anchorage, USA, October 9-12, 2011 

[22] F. Y. Ma, “Analysis of energy effinciency operational indicator of bulk 
carrier operational data using gray relation method”, Journal of 
Oceanography and Marine Science, Vol. 5, No. 4, pp. 30-36, 2014 

[23] S. Greco, M. Ehrgott, J. R. Figueira, Multiple Criteria Decision 
Analysis: State of the Art Surveys, Springer, 2005 

[24] S. C. Deshmukh, “Preference Ranking Organization Method Of 
Enrichment Evaluation Promethee”, International Journal of Engineering 
Science Invention, Vol. 2, No. 11, pp. 28-34, 2013 

[25] M. Zare, C. Pahl, H. Rahnama, M. Nilashi, A. Mardani, O. Ibrahim, H. 
Ahmadi, “Multi-criteria decision making approach in E-learning: A 
systematic review and classification”, Applied Soft Computing, Vol. 45, 
pp. 108-128, 2016