Interval PROMETHEE II, TOPSIS and EDAS Approaches for Multi-Criteria Ranking Problem of the Bank Branches in Iran
Subject Areas : Fuzzy Optimization and Modeling Journal
Saeid
Torkan
1
(Department of Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran)
Ali
Mahmoodirad
2
(Department of Mathematics, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran)
Sadegh
Niroomand
3
(Department of Industrial Engineering, Firouzabad Institute of
Higher Education, Firouzabad, Fars, Iran)
Saeid
Ghane
4
(Department of Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran)
Keywords: Multi-criteria Decision Making, Ranking Branches of Bank, Interval Value, Uncertainty,
Abstract :
Undoubtedly, rating bank branches is one of the essential tool managers use to promote branches. In this study, a multi-criteria problem applied in banking has been addressed. In this research, a framework for ranking 20 branches of Tose’e Ta'avon bank in Iran (Khuzestan province) using decision-making methods has been considered as a case study. Essential criteria are selected through experts and research literature. Then, according to the uncertainty in some indicators and the elimination of defects related to the investigation at a certain point, the data is determined in the form of interval values. The weighting of the criteria using experts' opinions, interval Shannon entropy, and the linear combination of the two, and considering the final matrix extracted from three 4-month intervals (geometric mean of 3 matrices) using three approaches, namely PROMETHEE II, EDAS, and TOPSIS with interval values, the ranking of bank branches has been used for a case study. Then, benchmark tests are used to validate the methods to provide a fairer ranking. Finally, the managers can see the actual position of the branches in identify throughout the year and use it to improve the bank's performance.
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Interval PROMETHEE II, TOPSIS and EDAS Approaches for Multi-Criteria Ranking Problem of the Bank Branches in Iran
Saeed Torkana , Ali Mahmoodiradb, Sadegh Niroomandc, Saeid Ghanea
a Department of Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran
b Department of Mathematics, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
c Department of Industrial Engineering, Firouzabad Higher Education Center, Shiraz University of Technology, Shiraz, Iran
A R T I C L E I N F O |
| A B S T R A C T Undoubtedly, rating bank branches is one of the essential tool managers use to promote branches. In this study, a multi-criteria problem applied in banking has been addressed. In this research, a framework for ranking 20 branches of Tose’e Ta'avon bank in Iran (Khuzestan province) using decision-making methods has been considered as a case study. Essential criteria are selected through experts and research literature. Then, according to the uncertainty in some indicators and the elimination of defects related to the investigation at a certain point, the data is determined in the form of interval values. The weighting of the criteria using experts' opinions, interval Shannon entropy, and the linear combination of the two, and considering the final matrix extracted from three 4-month intervals (geometric mean of 3 matrices) using three approaches, namely PROMETHEE II, EDAS, and TOPSIS with interval values, the ranking of bank branches has been used for a case study. Then, benchmark tests are used to validate the methods to provide a fairer ranking. Finally, the managers can see the actual position of the branches in identify throughout the year and use it to improve the bank's performance.
|
Article history: Received 1 January 2024 Revised 13 March 2024 Accepted 6 April 2024 Available online 29 April 2024 | ||
Keywords: Multi-criteria Decision-Making Ranking Banches of Bank Interval Value Uncertainty
|
1. Introduction
The proper functioning of the banking system plays a decisive role in improving economic activities. The importance of banks in countries' economies is undeniable as they provide diverse duties and services to society, including opening accounts, issuing various types of guarantees, loan payments, financial supply, electronic banking, etc. Ranking bank branches in terms of performance is an appropriate measure that can qualitatively and quantitatively improve branches in the prevailing competitive environment and ultimately assist managers in their future decision-making. It can also increase branch efficiency and provide better services to society. Numerous criteria are used to examine the situation of bank branches, and factors such as the time and method of extracting data, the uncertainty of some data, the weights assigned to each criterion, and the ranking methodology can complicate the issue of evaluating bank branches. However, these issues can be addressed using multi-criteria decision-making methods.
The selection of criteria for ranking bank branches plays a decisive role mainly influenced by branches' income, costs, profits, and losses. A literature review in this field points to the studies by Kumbirai and Webb [19] and Bičo and Ganić [4], which considered criteria such as capital amounts, return on assets, liquidity, etc., for evaluating and ranking the banking sector (See [30]). The literature generally addresses the issue using the CAMEL framework criteria to evaluate banking sector performance [1,12, 23, 32, 35].
These criteria are categorized into several main groups: capital-based, asset quality, management, profit-based, and liquidity-based. The CAMEL criteria are widely used for ranking banks. However, for ranking bank branches (banks' subsidiaries), some criteria impact the CAMEL index and are realized in each branch. According to this research, these consist of multiple criteria that are considered the most important based on expert opinions and literature in the subject field.
Generally, our work seeks to address the following research question. What are the rating indicators of Tose’e Ta'avon bank branches in Iran (Khuzestan province) and how can they is ranked?
Another essential consideration in ranking bank branches is choosing an appropriate methodology. Various multi-criteria decision-making approaches can be selected, as outlined in the literature. Commonly used approaches include AHP, TOPSIS, ELECTRE, PROMETHEE, SAW, MULTIMOORA, EDAS, etc.
Niroomand et al. [26] proposed a SAW-based approach for ranking countries by financial credibility. Kosmidou and Zopounidis [18] presented the PROMETHEE approach to measure banking sector performance. Brauers et al. [8] considered CAMEL-based criteria and a MULTIMOORA-based approach for evaluating Lithuanian banks. Ginevičius and Podviezko [13] also focused on banking sector evaluation using TOPSIS. Bilbao-Terol and Colleagues [5] also used the TOPSIS approach to evaluate government bonds in terms of sustainability. A decision support system was developed by Doumpos and Zopounidis [10]. Hemmati et al. integrated DEA and TOPSIS in their research [15]. Wątróbski [35] utilized PROMETHEE II method for multi-criteria sustainability assessment.
Moreover, to analyze the financial performance of banks in Serbia, Mandic and colleagues [23] used the integrated AHP and TOPSIS methods to evaluate the performance of the banking sector. Classical MCDM can be combined for better outcomes. Combined MCDM approaches have also been applied to banking issues. For example, Wu et al. [36] introduced an MCDM framework combining AHP, SAW, TOPSIS, and VIKOR for evaluating banking performance in fuzzy environments. Shavardi et al. [31] proposed a fuzzy MCDM and BSC framework for private Iranian banking. Beheshtinia and Amidi [2] used the AHP approach and VIKOR and TOPSIS methods to evaluate and rank issues in the banking sector. They also divided the study's main criteria into two parts: internal and external.
Due to computational similarities, Ozkalipci et al. [33], considered an integrated MCDM model to evaluate publicly traded Turkish banks using EDAS, MOORA, OCRA, and TOPSIS. In summary, various individual and combined MCDM approaches have been employed in the literature for banking sector analysis and branch ranking issues.
This study utilizes a multi-criteria decision-making (MCDM) framework employing range-based PROMETHEE, EDAS, and TOPSIS approaches to evaluate and rank bank branches in Khuzestan province, Iran. The internal criteria include financial, customer, administrative, and learning and growth areas, and external criteria include social and environmental. Essential aspects of this research include:
· The problem focuses on the multi-criteria ranking of Tose'e Ta'avon bank branches in Khuzestan province.
· Banking experts' opinions were consulted to select critical evaluation criteria based on the CAMEL methodology and other performance factors relevant to ranking branches.
· Historical branch data from previous years is used for ranking to facilitate more robust decision-making. As a result, three decision matrices with range values related to three 4-month periods will be considered in this analysis.
· Shannon entropy distance, experts' opinions, and a linear combination of the two are employed to determine the weights of the evaluation criteria.
· Classic PROMETHEE II, EDAS, and TOPSIS approaches are extended in a range-based form for branch ranking.
This paper is structured as follows: Section 2 describes the characteristics of the multi-criteria ranking problem for Tose'e Ta'avon bank branches in Khuzestan. Section 3 outlines the proposed solution methodology, including criteria weighting and the MCDM approaches applied. Section 4 presents computational results from the case study and validation. Section 5 provides concluding remarks.
2. The Multi-Criteria Ranking Problem of Bank Branches in Iran (Khuzestan Province)
As mentioned, this research examines the multi-criteria ranking problem of Tose'e Ta'avon bank branches in Khuzestan province, Iran. This problem was formed in three stages, as shown in Figure 1.
Figure 1. Stages of defining the multi-criteria ranking problem of bank branches in Khuzestan province, Iran
The steps in Figure 1 are explained in more detail below:
2.1. The branches of Tose'e Ta'avon Bank in Khuzestan province, Iran.
The Tose’e Ta’avon bank branches have been established in different cities of Khuzestan Province of Iran. Table 1 shows the location along with the code of these branches in Khuzestan province.
Table 1. The branches of the Tose’e Ta’avon bank in Khuzestan province of Iran
Branch code | Detailed name |
Br-1 | Ahvaz (Central) |
Br-2 | Dezfool |
Br-3 | Abadan |
Br-4 | Behbahan |
Br-5 | Masjed-Soleiman |
Br-6 | Ramhormoz |
Br-7 | Bandar-Mahshahr |
Br-8 | Izeh |
Br-9 | Shooshtar |
Br-10 | Andimeshk |
Br-11 | Khorramshahr |
Br-12 | Soosangerd |
Br-13 | Shadegan |
Br-14 | Shoosh |
Br-15 | Bagh-malek |
Br-16 | Ahvaz (Kiyanpars) |
Br-17 | Omidiyeh |
Br-18 | Hendijan |
Br-19 | Lali |
Br-20 | Ahvaz (Taleghani) |
2.2. Important Evaluation Criteria
The most important and influential criteria for ranking bank branches based on the literature discussed in section one, as well as criteria based on the CAMEL methodology and other determining factors identified by a panel of experts from Tose'e Ta'avon bank, were selected. As a result, 15 criteria for evaluating and ranking bank branches, shown in Table 2, were chosen based on feedback from the literature and expert opinions. These criteria are divided into two categories: positive criteria, where higher values are preferable, and negative criteria, where lower values are desirable.
Table 2. The criteria selected for the proposed multi-criteria ranking problem
Criterion code | Description | Type |
C-1 | NPL, which is obtained from total loans paid divided by all charges of a branch | Negative |
C-2 | Common income per employee | Positive |
C-3 | Non-common income per employee | Positive |
C-4 | Non-performing loans per employee | Negative |
C-5 | Low-cost resources | Positive |
C-6 | Deposited resources | Positive |
C-7 | Amount of bank guarantee | Positive |
C-8 | Benefit | Positive |
C-9 | Total cost price of the services | Negative |
C-10 | Total bank advances paid to customers per employee | Positive |
C-11 | Total bank account balance per employee | Positive |
C-12 | Income from POS machines | Positive |
C-13 | Average income from ATM machines | Positive |
C-14 | Number of customers per employee | Positive |
C-15 | Number of electronic customers per employee | Positive |
2.3. Decision Matrices
This subsection determines the decision matrix for the proposed multi-criteria ranking model of the Tose'e Ta'avon bank branches in Khuzestan province, Iran. This is an m×n dimensional matrix where m represents the number of branches (m=20), and n represents the number of criteria (n=15). The final values in this matrix are the geometric means of the values from three separate matrices, including performance data for the Tose'e Ta'avon bank branches in Khuzestan over three or four months. The first matrix contains data on each branch's performance against the key indices during the first quarter of the year (Farvardin to Tir). The second matrix has data for the second quarter (Mordad to Aban).
Moreover, the third matrix covers the third quarter (Azar to Esfand). The overall decision matrix is obtained by calculating the geometric mean of these three periodic matrices. This decision matrix is presented below:
(1)
In this matrix 
the performance range of branch i of the bank for the selected criterion j is presented.
Matrix 
contains interval numbers and shows each branch's annual performance range, where the range's lower and upper values indicate the minimum and maximum values extracted from the final matrix (geometric mean of values from the three 4-month matrices).
Considering the decision matrix in an interval form has the following benefits:
· Like other real-world problems, there is an element of uncertainty in this research issue. Representing the decision matrix as an interval form eliminates this uncertainty.
· Taking the performance ranges over the three 4-month periods and calculating their geometric mean can facilitate fairer and more robust decision-making.
The decision matrix presented is used to rank the bank branches shown in Table 1. The following section will discuss the methodology.
3. Solution Methodology
As mentioned earlier, the ranking problem in this study aims to evaluate and rank the branches of the Tose'e Ta'avon bank in Khuzestan province based on predefined criteria measured using interval values.
In this section, a two-phase solution methodology is proposed for conducting the ranking as follows:
Phase 1: The weights representing the importance of each criterion (internal, external, and combined weights) are determined.
Phase 2: The PROMETHEE II, EDAS, and TOPSIS interval-based ranking methods are extended to rank the bank branches.
The details of these phases will be explained in the following sections.
3.1. Phase 1 - Determining the Criteria Importance Weights
In this study, we propose an approach that considers both expert opinions and decision matrix data. The proposed methodology for determining the criteria importance weights is as follows:
We are determining the weights based on expert opinions, defined as external weights.
· Determining the weights using the interval-form Shannon entropy weighting method applied to the decision matrix data, defined as internal weights.
· Determining the weights as a combination of the internal and external weights, defined as combined weights.
This approach is illustrated in Figure 2.
Figure 2. The proposed process for determining the final weights
3.1.1. Determining External Weights (Step 1)
The process of determining the external weights of criteria j (denoted by 
) is explained below:
1. Select a number of expert bankers (denoted by s) who are knowledgeable about the banking sector.
2. Ask each expert to score each criterion denoted by 
, where 
represents the score for criterion j given by expert banker p.
3. Calculate the external importance weight of each criterion as 
. This provides the normalized weighted averages of the expert scores.
The above steps yield definitive and normalized weight values.
3.1.2. Determining Internal Weights (Step 2):
Next, the interval Shannon entropy approach proposed by Latifi and Falahati Nejad [21] is employed to calculate the interval internal weight values. The steps are:
1. Normalizing matrix A using equation (2) and (3) as follows:
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