multi-criteria decision making methods and their

Proceedings of the International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020

© IEOM Society International

Multi-Criteria Decision Making Methods And Their Applications– A Literature Review

Fatma Eltarabishi

Department of Industrial Management and Engineering Management, University of Sharjah

Sharjah, UAE [email protected] Omar Hassan Omar

Department of Industrial Management and Engineering Management, University of Sharjah

Sharjah, UAE [email protected]

Imad Alsyouf Department of Industrial Management and Engineering Management, Sustainable Engineering Asset Management (SEAM) Research Group,

University of Sharjah Sharjah, UAE

[email protected] Maamar Bettayeb

Electrical Engineering Department, University of Sharjah, Sharjah, UAE and CEIES, King Abdulaziz University, Jeddah, KSA

[email protected]

Abstract Multi-criteria decision-making is gaining high popularity in solving decision-making problems in various fields. Decision-makers use multi-criteria decision-making methods to solve problems daily due to their capability of decomposing complex problems to their simplest form. This paper aims to review the literature from 2015 to 2019 to analyze the most common methods used in real-world applications. A review of 89 articles is presented to conclude that Hybrid methods are the most commonly used techniques in real-world applications in the period of 2015 to 2019. This paper also discusses the change in the trend of multi-criteria decision-making methods when compared to Mardani's article. Keywords MCDM techniques, Hybrid methods, AHP, TOPSIS, ELECTRE. I. INTRODUCTION

Decision making is an intricate process in which organizations suffer to obtain the final desired outcome successfully. The decision-making process is the act of selecting the most suitable action to fulfill the desired goals and objectives [1]. Because decision making is a daily task in our everyday routines, effective tools should be used to analyze all aspects of decision-making problems. Multi-Criteria Decision Making (MCDM) is a well-structured and multidimensional process developed to tackle decision-making problems in different fields and search for the most attractive alternative with consideration of all relevant criteria. Due to its powerful tools, it analyzes complex decision-making problems in different fields. This method improves the quality of decision-making to become more rational and efficient [2]. Undoubtedly, MCDM has grown recently and been utilized in different fields such as sustainable energy[2,3], maintenance management [4,5], construction management[6], tourism management [7], machine selection [8], material selection [9], petroleum [10], supply chain management [11], aviation [12,13]and risk management [14]. MCDM methods are considered the most recommended tools when dealing with decision-making problems in various fields. Unfortunately, the identification of a single methodology in any field is difficult. Decision-makers can select different techniques for the same problem, and different results are approached. This is considered as the main limitation of MCDM [17]. For example, in the renewable energy field, Akash et al. [15] used the Analytical Hierarchy Process (AHP) to select a power plant in Jordan. However, Mladineo et al. [16] used PROMETHE to select a small hydro plant.

2654

mailto:[email protected]



MCDM is in continuous evolution every day. Hence, decision-makers should always review the literature to capture the updates and be able to improve their decision in real applications. To guide the decision-makers, we have updated Mardani et al. [18] paper that reviewed the use of MCDM methods and their development from 2000 to 2014. We believe that the use of MCDM methods has further developed in the last few years. Therefore, this study aims to review the literature from 2015 to 2019 to answer the following questions: (1) What are the most commonly MCDM methods used in real-world applications? (2) How is the trend of MCDM methods changing when compared to Mardani's article?

The organization of this research paper is as follows: Section II gives a brief overview of the MCDM methods in the literature. Section III discusses the methodology used in this study and presents the findings obtained by answering the research questions, and finally, Section IV presents the conclusion.

II. LITERATURE REVIEW OF MCDM METHODS

MCDM serves as an aid for decision-making but not to make the decision. In other words, MCDM does not prescribe how decisions should be made. It only leads to logical and reasonable decision rankings [71]. According to many authors, MCDM methods are classified into two groups due to the different problem settings [19]: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM).

MADM deals with decision problems that have an implicit objective and a discrete decision space (finite number of alternatives and attributes). On the other hand, MODM problems have explicit objectives and a continuous decision space (infinite number of alternatives and attributes). Not all multi-objective problems have well-defined alternatives. Therefore, different methods are applied based on the nature of decision problems. Figure 1 presents the most well-known and applied methods in various fields. However, other MCDM methods are still practiced, but most decision-makers refer to the methods in Figure 1.

Figure 1. MADM and MODM methods

Most MCDM decision problems deal with discrete decision space MADM [3]. Hence, only the most common MADM methods will be briefly discussed below.

Analytical Hierarchy Process (AHP) is one of the most frequently used methods in real situations due to its natural ease. AHP determines the weight criteria, ranks alternatives, or both simultaneously. Ratnayake and Markese [20] used AHP to select a maintenance strategy of oil and gas installation considering the health, safety, environmental, and financial criteria. Rajak and Shaw [21] applied AHP to weight criteria during the selection of ideal mobile health (mHealth) applications. Chourabi et al. [22] evaluated workforce selection problems in the apparel industry using AHP. Usually, decision-makers use AHP in complex

Multi-Criteria Decision Making (MCDM)

Multi-Attribute Decision Making (MADM)

Analytical Hierarchy Process (AHP) & Analytic Network Process (ANP)

Et Choice Translating Reality (ELECTRE)

VIšekriterijumsko KOmpromisno Rangiranje (VIKOR)

Weight Sum Model (WSM) & Weight Product Model (WPM)

Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

Decision Making Trial and Evaluation Laboratory (DEMATEAL)

Multi-Objective Decision Making (MODM)

linear Programming

Goal Programming

2655



problems due to its capability of decomposing any problem to a hierarchy structure, showing clearly the main goal, criteria, and sub-criteria that affect decision-making, and all the feasible alternatives. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a unique method that uses simple and logical-mathematical concepts. It is an easy and straightforward process [3]. The best alternative should have the shortest distance from the reference point while the furthest distance from the anti-ideal point in Euclidean space [8]. Compared to Weighed-Sum Procedure (WSP), TOPSIS is more sensitive, as Simanaviciene & Ustinovichius [21] stated. Štirbanović et al. [8] applied TOPSIS to select the most suitable flotation machine. In material selection problems, Mousavi-Nasab & Sotoudeh-Anvari [9] and Chakraborty & Chatterjee [23] applied the TOPSIS approach because of its comprehensiveness and simplicity. Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is an outranking method that estimates the strength of one alternative over the other. Different versions of PROMETHEE are applied based on the nature of the problem (i.e., PROMETHEE I for partial ranking and PROMETHEE II for full ranking). Mohamadabadi et al. [24] studied the most appropriate transportation fuel vehicle using PROMETHEE. Oberschmidt et al. [25] evaluated the energy technologies based on efficiency, cost, and availability criteria. Et Choice Translating Reality (ELECTRE) is another outranking method that uses pair-wise comparison to prefer an alternative, among others. This method eliminates the least favorable alternative, which makes it suitable for decision problems with few criteria and many alternatives. A significant criticism of ELECTRE is its long computational process when compared to other techniques [3]. Sites for subsurface dams construction were ranked by Dortaj et al. [26] using ELECTRE. Öztürk et al.[27] selected the most appropriate supplier for a cable company in Samsun using ELECTRE. The methodological process to achieve the aim of this study is illustrated as follows: Phase I presents a brief overview of Mardani et al. [18] paper. Phase II conducts a literature review for MCDM articles during the period from 2015 to 2019. Phase III classifies the MCDM identified articles based on the techniques used and the year of publication, as it was done in Mardani’s paper. Phase IV analyses the outcomes of Phase III and compares the results with Mardani's paper.

III. RESULTS AND DISCUSSION This section follows the methodological procedures introduced in Section II to obtain answers to the previously stated research questions. Phase I: Overview of Mardani’s paper Mardani et al. [18] reviewed a total of 393 articles from 2000 to 2014. The paper documented the development of MCDM methods. One of the main results of the paper is the frequency distribution of the most used MCDM methods, as shown in Figure 2. The methods were listed from the most frequently used method to the least frequently used one.

Figure 2. Mardani's distribution of MCDM methods

Mardani et al. [18] classified the articles based on the use of MCDM methods, including; AHP, TOPSIS, ELECTRE, ANP, PROMETHEE, DEMATEL, VIKOR, hybrid MCDM, and DM aggregation methods. As Mardani’s paper stated, hybrid methods involve the combination of two or more different methods for the sake of obtaining better results. Aggregate methods are Complex Proportional Assessment (COPRAS), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment

128

6446 45 34 29 26

14 70

20406080

100120140

2656



(WASPAS), Step-wise Weight Assessment Ratio Analysis (SWARA) and Multi-Objective Optimization by Ratio Analysis (MOORA or MULTIMOORA). MULTIMOORA is the updated version of MOORA. Phase II: Literature review results from 2015 to 2019. In this study, like the method adopted by Mardani's paper and based on its outcomes, we conducted a literature review for the MCDM articles published in scientific databases during the period starting from 2015 to 2019. The aim is to map out the most common decision-making techniques published over the last five years. The research used most keywords that were used in Mardani's paper: PROMETHEE, DEMATEL, AHP, TOPSIS, ELECTRE, VIKOR, ARAS, ANP, and COPRAS. As a result, we selected 89 articles from different fields that deal with different decision-making problems. The selection of these articles was based on their relevance, citation number, and whether each paper discusses the application of MCDM thoroughly. This literature review is not exhaustive, it is planned to cover more methods and extend the paper selection criteria in a full journal paper. Phase III: Classification of searched articles For the scope of this study, each article is classified based on the year of publication and the MCDM method used to tackle the decision-making problem. Distribution of MCDM papers over time While the articles were intensified between the years 2015 and 2019, it is observed in Figure 3 that generally, MCDM methods have more interest throughout the years. It can be predicted that the use of MCDM tools will increase in all decision-making problems due to its growing trend.

Figure 3. Distribution of the number of MCDM publications over time.

Distribution based on MCDM approaches Figure (4) presents the distribution of the 89 articles based on MCDM methods. Based on the 89 articles researched, the top-ranked MCDM methods used are the hybrid methods with 28 articles (32%), AHP follows with 19 articles (21%), and aggregated methods with 16 articles (18%). ELECTRE and TOPSIS are almost equally ranked. Finally, the other MCDM methods are shown in Figure 4.

Figure 4. Distribution of publications based on MCDM methods

It is clearly displayed that hybrid methods are obtaining a well-known reputation as Mardani’s paper predicted. Noticeably, many articles that used the hybrid method, Özcan et al. [50], He et al. [35], Rajak & Shaw [53], Abdel-Malak et al. [52], Parezanović et al. [60] and Kumar et al. [70] combined AHP for weight criteria and TOPSIS for alternative ranking. The majority of hybrid methods use AHP to weigh criteria, while only five articles used other methods (BWM or ANP). Decision-makers find it challenging to select a single method as each has its pros and cons [41]. Therefore, decision-makers find it advantageous to combine two methods to increase the pros of both methods and try to mitigate the cons as much as possible. While researching

10

2116

20 22

0

10

20

30

2015 2016 2017 2018 2019

28

1916

9 84 2 2 1

05

1015202530

2657



ANP, VIKOR, DEMATEL and PROMETHEE, it was difficult to find articles that used each method individually to obtain a result. On the other hand, many papers [55,62,71] combined these methods with other methods (Hybrid methods).

Phase IV: Comparison of results with Mardani’s paper The results in Table 1 help in answering the second research question. It shows how the trend of the used methods changed throughout the years. Hybrid methods have developed well to become the first and most appropriate choice for decision-makers. Although TOPSIS is characterized by its simple process, ease of use, and high computational efficiency [72,17], its use became scarce recently. Similarly, the ELECTRE method is ranked just after TOPSIS, although it is also highly efficient [72]. Surprisingly, TOPSIS and ELECTRE have not advanced nor became less useful. However, they stayed stable in both periods. According to Amirshenava & Osanloo [56], PROMETHEE is devoid of ELECTRE limitations such as complex calculations and a time-consuming process. However, PROMETHEE is ranked after ELECTRE in both results.

Table 1. Comparison of the rank of MCDM methods over time. 2000-2014 2015-2019 AHP 1 2 TOPSIS 4 4 ELECTRE 5 5 Hybrid MCDM 2 1 Aggregation methods 3 3 ANP 6 7 PROMETHEE 7 6 VIKOR 8 8 DEMATEL 9 9

The usage of single methods such as AHP, ELECTRE, and TOPSIS purely are becoming less popular in solving decision-making problems when compared to previous years. Hybrid MCDM is the leading method where most decision-makers refer to it for more efficient selections. Ghenai et al. [68] stated that the SWARA method improves the criteria prioritization process, which means more reliable decision-making. Therefore, Aggregation methods are suggested to be the new leading technique in the next upcoming years due to their higher efficiency and reliability. IV. CONCLUSION A review of the published literature in MCDM methods used over the last five years was presented in this paper. The use of MCDM methods is in continuous development throughout the years. Thus, observing the trends in solving decision-making problems is essential. In this paper, the authors conducted an updated literature review and analyzed the identified papers. It was found that the most common methods used in real-world applications are hybrid methods. This result confirms what Mardani's paper predicted. Mardani's paper results showed that the AHP method was the most common method from 2000 to 2014. In our results, we concluded that hybrid methods were ranked first during the period 2015 to 2019, and AHP methods were ranked second; the answer to our second research question. Overall, this study suggests that, in the coming future, aggregate methods will be used more regularly due to their higher efficiency and effectiveness.

REFERENCES [1] M. Haddad and D. Sanders, “Selection of discrete multiple criteria decision making methods in the presence of risk and

uncertainty,” Oper. Res. Perspect., vol. 5, no. August, pp. 357–370, 2018. [2] S. D. Pohekar and M. Ramachandran, “Application of multi-criteria decision making to sustainable energy planning - A review,”

Renew. Sustain. Energy Rev., vol. 8, no. 4, pp. 365–381, 2004. [3] I. Siksnelyte, E. K. Zavadskas, D. Streimikiene, and D. Sharma, “An overview of multi-criteria decision-making methods in

dealing with sustainable energy development issues,” Energies, vol. 11, no. 10, 2018. [4] B. Al-Najjar and I. Alsyouf, “Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making,”

Int. J. Prod. Econ., vol. 84, no. 1, pp. 85–100, 2003. [5] M. Shafiee, “Maintenance strategy selection problem: An MCDM overview,” J. Qual. Maint. Eng., vol. 21, no. 4, pp. 378–402,

2015. [6] D. Jato-Espino, E. Castillo-Lopez, J. Rodriguez-Hernandez, and J. C. Canteras-Jordana, “A review of application of multi-

criteria decision making methods in construction,” Automation in Construction, vol. 45. pp. 151–162, 2014. [7] A. Akincilar and M. Dagdeviren, “A hybrid multi-criteria decision making model to evaluate hotel websites,” Int. J. Hosp.

Manag., vol. 36, pp. 263–271, 2014. [8] Z. Štirbanović, D. Stanujkić, I. Miljanović, and D. Milanović, “Application of MCDM methods for flotation machine selection,”

Miner. Eng., vol. 137, no. October 2018, pp. 140–146, 2019.

2658



[9] S. H. Mousavi-Nasab and A. Sotoudeh-Anvari, “A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems,” Mater. Des., vol. 121, pp. 237–253, 2017.

[10] M. H. Shahsavari and E. Khamehchi, “Optimum selection of sand control method using a combination of MCDM and DOE techniques,” J. Pet. Sci. Eng., vol. 171, no. January, pp. 229–241, 2018.

[11] W. Ho, X. Xu, and P. K. Dey, “Multi-criteria decision making approaches for supplier evaluation and selection: A literature review,” Eur. J. Oper. Res., vol. 202, no. 1, pp. 16–24, 2010.

[12] M. Janic and A. Reggiani, “An application of the multiple criteria decision making (MCDM) analysis to the selection of a new Hub Airport,” Eur. J. Transp. Infrastruct. Res. EJTIR, 2, no. Mcdm, 2002.

[13] P. J. Gudiel Pineda, J. J. H. Liou, C. C. Hsu, and Y. C. Chuang, “An integrated MCDM model for improving airline operational and financial performance,” J. Air Transp. Manag., vol. 68, pp. 103–117, 2018.

[14] M. Ilangkumaran, M. Karthikeyan, T. Ramachandran, M. Boopathiraja, and B. Kirubakaran, “Risk analysis and warning rate of hot environment for foundry industry using hybrid MCDM technique,” Saf. Sci., vol. 72, pp. 133–143, 2014.

[15] B. A. Akash, R. Mamlook, and M. S. Mohsen, “Multi-criteria selection of electric power plants using analytical hierarchy process,” Electr. Power Syst. Res., vol. 52, no. 1, pp. 29–35, 1999.

[16] N. Mladineo, J. Margeta, J. P. Brans, and B. Mareschal, “Multicriteria ranking of alternative locations for small scale hydro plants,” Eur. J. Oper. Res., vol. 31, no. 2, pp. 215–222, 1987.

[17] E. Mulliner, N. Malys, and V. Maliene, “Comparative analysis of MCDM methods for the assessment of sustainable housing affordability,” Omega (United Kingdom), vol. 59, pp. 146–156, 2016.

[18] N. Z. and A. V. Abbas Mardani, Ahmad Jusoh, Khalil MD Nor, Zainab Khalifah, Norhayati Zakwana and, Alireza Valipour, “Multiple Criteria Decision Making Techniques and Its Applications– A Review of the Literature from 2000 to 2014.” pp. 516–571, 2015.

[19] E. Triantaphyllou, B. Shu, S. N. Sanchez, and T. Ray, “Multi-Criteria Decision Making : An Operations Research Approach,” Electronics, vol. 15, pp. 175–186, 1998.

[20] R. M. C. Ratnayake and T. Markeset, “Methodology and theory: Technical integrity management: Measuring HSE awareness using AHP in selecting a maintenance strategy,” J. Qual. Maint. Eng., vol. 16, no. 1, pp. 44–63, 2010.

[21] R. Simanaviciene and L. Ustinovichius, “Sensitivity analysis for multiple criteria decision making methods: TOPSIS and SAW,” Procedia - Soc. Behav. Sci., vol. 2, no. 6, pp. 7743–7744, 2010.

[22] A. B. and M. C. Zouhour Chourabi , Faouzi Khedher, “Multi-criteria decision making in workforce choice using AHP, WSM and WPM.” pp. 1092–1101, 2018.

[23] S. C. and P. Chatterjeeb, “Selection of materials using multi-criteria decision-making methods with minimum data.” pp. 135–148, 2013.

[24] H. Safaei Mohamadabadi, G. Tichkowsky, and A. Kumar, “Development of a multi-criteria assessment model for ranking of renewable and non-renewable transportation fuel vehicles,” Energy, vol. 34, no. 1, pp. 112–125, 2009.

[25] M. J. Oberschmidt, P. J. Geldermann, M. J. Ludwig, and M. M. Schmehl, “Modified PROMETHEE Approach to Assessing Energy Technologies,” Int. J. Energy Sect. Manag., vol. 4, no. 2, 2010.

[26] A. Dortaj, S. Maghsoudy, F. Doulati Ardejani, and Z. Eskandari, “A hybrid multi-criteria decision making method for site selection of subsurface dams in semi-arid region of Iran,” Groundw. Sustain. Dev., vol. 10, no. April 2018, p. 100284, 2020.

[27] E. Pekel and B. Elevli, “ANP ve ELECTRE Y öntemleri Kullanılarak Tedarikçi Seçimi : Kablo Sektörü U ygulaması Using ANP and ELECTRE Methods for Supplier Selection : Cable Industry Application,” 2017.

[28] J. Si, L. Marjanovic-Halburd, F. Nasiri, and S. Bell, “Assessment of building-integrated green technologies: A review and case study on applications of Multi-Criteria Decision Making (MCDM) method,” Sustain. Cities Soc., vol. 27, pp. 106–115, 2016.

[29] R. Shad, M. Khorrami, and M. Ghaemi, “Developing an Iranian green building assessment tool using decision making methods and geographical information system: Case study in Mashhad city,” Renew. Sustain. Energy Rev., vol. 67, pp. 324–340, 2017.

[30] M. AlSabbagh, Y. L. Siu, A. Guehnemann, and J. Barrett, “Integrated approach to the assessment of CO2e-mitigation measures for the road passenger transport sector in Bahrain,” Renew. Sustain. Energy Rev., vol. 71, pp. 203–215, 2017.

[31] A. Al-Qudaimi and A. Kumar, “Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia: necessary modifications,” Int. J. Sustain. Energy, vol. 37, no. 5, pp. 436–437, 2018.

[32] M. Claudia Roldán, M. Martínez, and R. Peña, “Scenarios for a hierarchical assessment of the global sustainability of electric power plants in México,” Renew. Sustain. Energy Rev., vol. 33, pp. 154–160, 2014.

[33] G. Blanco, R. Amarilla, A. Martinez, C. Llamosas, and V. Oxilia, “Energy transitions and emerging economies: A multi-criteria analysis of policy options for hydropower surplus utilization in Paraguay,” Energy Policy, vol. 108, no. June, pp. 312–321, 2017.

[34] M. Tahri, M. Hakdaoui, and M. Maanan, “The evaluation of solar farm locations applying Geographic Information System and Multi-Criteria Decision-Making methods: Case study in southern Morocco,” Renew. Sustain. Energy Rev., vol. 51, pp. 1354–1362, 2015.

[35] C. He, Q. Zhang, J. Ren, and Z. Li, “Combined cooling heating and power systems: Sustainability assessment under uncertainties,” Energy, vol. 139, pp. 755–766, 2017.

[36] S. Ghosh, T. Chakraborty, S. Saha, M. Majumder, and M. Pal, “Development of the location suitability index for wave energy production by ANN and MCDM techniques,” Renew. Sustain. Energy Rev., vol. 59, pp. 1017–1028, 2016.

[37] S. Jovčić, P. Průša, J. Samson, and D. Lazarević, “a Fuzzy- Ahp Approach To Evaluate the Criteria of Third -Party Logistics (3Pl) Service Provider,” Int. J. Traffic Transp. Eng., vol. 9, no. 3, pp. 280–289, 2019.

2659



[38] A. A. Khan, M. Shameem, R. R. Kumar, S. Hussain, and X. Yan, “Fuzzy AHP based prioritization and taxonomy of software process improvement success factors in global software development,” Appl. Soft Comput. J., vol. 83, p. 105648, 2019.

[39] I. Miciuła and J. Nowakowska-Grunt, “Using the AHP method to select an energy supplier for household in Poland,” Procedia Comput. Sci., vol. 159, no. October, pp. 2324–2334, 2019.

[40] A. Jamshidi, F. Jamshidi, D. Ait-Kadi, and A. Ramudhin, “A review of priority criteria and decision-making methods applied in selection of sustainable city logistics initiatives and collaboration partners,” Int. J. Prod. Res., vol. 57, no. 15–16, pp. 5175–5193, 2019.

[41] M. Baumann, M. Weil, J. F. Peters, N. Chibeles-Martins, and A. B. Moniz, “A review of multi-criteria decision making approaches for evaluating energy storage systems for grid applications,” Renew. Sustain. Energy Rev., vol. 107, no. January, pp. 516–534, 2019.

[42] M. CRISTEA and C. CRISTEA, “a Multicriteria Decision-Making Approach Used for the Selection of a Logistics Center Location,” Ann. ORADEA Univ. Fascicle Manag. Technol. Eng., vol. Volume XXV, no. 1, 2016.

[43] T. Uysal and K. Yavuz, “Selection of Logistics Centre Location via ELECTRE Method : A Case Study in Turkey,” Int. J. Bus. Soc. Sci., vol. 5, no. 9, pp. 276–289, 2014.

[44] D. K. Singh and P. Kaushik, “Intrusion response prioritization based on fuzzy ELECTRE multiple criteria decision making technique,” J. Inf. Secur. Appl., vol. 48, p. 102359, 2019.

[45] A. Debnath, M. Majumder, and M. Pal, “Potential of Fuzzy-ELECTRE MCDM in Evaluation of Cyanobacterial Toxins Removal Methods,” Arab. J. Sci. Eng., vol. 41, no. 10, pp. 3931–3944, 2016.

[46] M. Erdem and T. E. Erkan, “Evaluation of Supply Chain Performance Using an Integrated Two-Step Clustering and Interval Type-2 Fuzzy Topsis Method: a Case Study,” Pamukkale Univ. J. Soc. Sci. Inst., vol. 8326, 2019.

[47] P. Průša, S. Jovčić, V. Němec, and P. Mrázek, “Forklift Truck Selection Using Topsis Method,” Int. J. Traffic Transp. Eng., vol. 8, no. 3, pp. 390–398, 2018.

[48] E. O. Diemuodeke, S. Hamilton, and A. Addo, “Multi-criteria assessment of hybrid renewable energy systems for Nigeria’s coastline communities,” Energy. Sustain. Soc., vol. 6, no. 1, 2016.

[49] R. Gao, H. O. Nam, W. Il Ko, and H. Jang, “Integrated system evaluation of nuclear fuel cycle options in China combined with an analytical MCDM framework,” Energy Policy, vol. 114, no. December 2017, pp. 221–233, 2018.

[50] E. C. Özcan, S. Ünlüsoy, and T. Eren, “A combined goal programming – AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants,” Renew. Sustain. Energy Rev., vol. 78, no. June, pp. 1410–1423, 2017.

[51] A. Karaşan and C. Kahraman, “A novel intuitionistic fuzzy DEMATEL - ANP - TOPSIS integrated methodology for freight village location selection,” J. Intell. Fuzzy Syst., vol. 36, no. 2, pp. 1335–1352, 2019.

[52] F. F. Abdel-malak, U. H. Issa, Y. H. Miky, and E. A. Osman, “Applying decision-making techniques to Civil Engineering Projects,” Beni-Suef Univ. J. Basic Appl. Sci., vol. 6, no. 4, pp. 326–331, 2017.

[53] M. Rajak and K. Shaw, “Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS,” Technol. Soc., vol. 59, no. July, p. 101186, 2019.

[54] R. Micale, C. M. La Fata, and G. La Scalia, “A combined interval-valued ELECTRE TRI and TOPSIS approach for solving the storage location assignment problem,” Comput. Ind. Eng., vol. 135, no. December 2018, pp. 199–210, 2019.

[55] C. M. Sivaraja and G. Sakthivel, “Compression ignition engine performance modelling using hybrid MCDM techniques for the selection of optimum fish oil biodiesel blend at different injection timings,” Energy, vol. 139, pp. 118–141, 2017.

[56] S. Amirshenava and M. Osanloo, “Mine closure risk management: An integration of 3D risk model and MCDM techniques,” J. Clean. Prod., vol. 184, pp. 389–401, 2018.

[57] A. Arabameri, B. Pradhan, K. Rezaei, and C. Conoscenti, “Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques,” Catena, vol. 180, no. September 2018, pp. 282–297, 2019.

[58] Z. P. Tian, H. Y. Zhang, J. Q. Wang, and T. L. Wang, “Green supplier selection using improved TOPSIS and best-worst method under intuitionistic fuzzy environment,” Inform., vol. 29, no. 4, pp. 773–780, 2018.

[59] Y. A. Turker, K. Baynal, and T. Turker, “The evaluation of learning management systems by using Fuzzy AHP, fuzzy topsis and an integrated method: A case study,” Turkish Online J. Distance Educ., vol. 20, no. 2, pp. 195–218, 2019.

[60] T. P. Ć, “A multi-criteria decision making approach for evaluating sustainable city logistics measures,” pp. 1–6. [61] A. Fetanat, H. Mofid, M. Mehrannia, and G. Shafipour, “Informing energy justice based decision-making framework for waste-

to-energy technologies selection in sustainable waste management: A case of Iran,” J. Clean. Prod., vol. 228, pp. 1377–1390, 2019.

[62] S. Tadić, S. Zečević, and M. Krstić, “A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection,” Expert Syst. Appl., vol. 41, no. 18, pp. 8112–8128, 2014.

[63] Z. Wang, G. Xu, H. Wang, and J. Ren, “Distributed energy system for sustainability transition: A comprehensive assessment under uncertainties based on interval multi-criteria decision making method by coupling interval DEMATEL and interval VIKOR,” Energy, vol. 169, pp. 750–761, 2019.

[64] W. Serrai, A. Abdelli, L. Mokdad, and Y. Hammal, “Towards an efficient and a more accurate web service selection using MCDM methods,” J. Comput. Sci., vol. 22, pp. 253–267, 2017.

[65] Y. Bahrami, H. Hassani, and A. Maghsoudi, “BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran,” Spat. Stat., vol. 33, 2019.

[66] T. Baležentis and D. Streimikiene, “Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation,” Appl.

2660



Energy, vol. 185, no. 2017, pp. 862–871, 2017. [67] H. S. Dhiman, D. Deb, V. Muresan, and M. L. Unguresan, “Multi-criteria decision making approach for hybrid operation of

wind farms,” Symmetry (Basel)., vol. 11, no. 5, 2019. [68] C. Ghenai, M. Albawab, and M. Bettayeb, “Sustainability indicators for renewable energy systems using multi-criteria decision-

making model and extended SWARA/ARAS hybrid method,” Renew. Energy, vol. 146, pp. 580–597, 2020. [69] M. Pavlovskis, J. Antucheviciene, and D. Migilinskas, “Assessment of Buildings Redevelopment Possibilities using MCDM

and BIM Techniques,” Procedia Eng., vol. 172, pp. 846–850, 2017. [70] R. R. Kumar, S. Mishra, and C. Kumar, “Prioritizing the solution of cloud service selection using integrated MCDM methods

under Fuzzy environment,” J. Supercomput., vol. 73, no. 11, pp. 4652–4682, 2017. [71] H. Gupta, “Evaluating service quality of airline industry using hybrid best worst method and VIKOR,” J. Air Transp. Manag.,

vol. 68, pp. 35–47, 2018. [72] M. H. Saad, M. A. Nazzal, and B. M. Darras, “A general framework for sustainability assessment of manufacturing processes,”

Ecol. Indic., vol. 97, no. October 2018, pp. 211–224, 2019. [73] H. Veisi, H. Liaghati, and A. Alipour, “Developing an ethics-based approach to indicators of sustainable agriculture using

analytic hierarchy process (AHP),” Ecol. Indic., vol. 60, pp. 644–654, 2016. [74] E. Koç and H. A. Burhan, “An Application of Analytic Hierarchy Process (AHP) in a Real World Problem of Store Location

Selection,” Adv. Manag. Appl. Econ., vol. 5, no. 1, pp. 41–50, 2015. [75] S. Shekhar and A. C. Pandey, “Delineation of groundwater potential zone in hard rock terrain of India using remote sensing,

geographical information system (GIS) and analytic hierarchy process (AHP) techniques,” Geocarto Int., vol. 30, no. 4, pp. 402–421, 2015.

[76] S. Thanki, K. Govindan, and J. Thakkar, “An investigation on lean-green implementation practices in Indian SMEs using analytical hierarchy process (AHP) approach,” J. Clean. Prod., vol. 135, pp. 284–298, 2016.

[77] O. Rahmati, A. Nazari Samani, M. Mahdavi, H. R. Pourghasemi, and H. Zeinivand, “Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS,” Arab. J. Geosci., vol. 8, no. 9, pp. 7059–7071, 2015.

[78] A. Kumar and A. P. Krishna, “Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach,” Geocarto Int., vol. 33, no. 2, pp. 105–129, 2018.

[79] A. Cahyapratama and R. Sarno, “Application of Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods in singer selection process,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-January, no. Mcdm, pp. 234–239, 2018.

[80] M. Gökhan Yücel and A. Görener, “Decision making for company acquisition by ELECTRE method,” Int. J. Supply Chain Manag., vol. 5, no. 1, pp. 75–83, 2016.

[81] A. Fahmi, C. Kahraman, and Ü. Bilen, “ELECTRE I Method Using Hesitant Linguistic Term Sets: An Application to Supplier Selection,” Int. J. Comput. Intell. Syst., vol. 9, no. 1, pp. 153–167, 2016.

[82] M. Rohaninejad, A. Kheirkhah, P. Fattahi, and B. Vahedi-Nouri, “A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem,” Int. J. Adv. Manuf. Technol., vol. 77, no. 1–4, pp. 51–66, 2015.

[83] L. Fei, J. Xia, Y. Feng, and L. Liu, “An ELECTRE-Based Multiple Criteria Decision Making Method for Supplier Selection Using Dempster-Shafer Theory,” IEEE Access, vol. 7, pp. 84701–84716, 2019.

[84] N. Chen, Z. Xu, and M. Xia, “The ELECTRE I multi-criteria decision-making method based on hesitant fuzzy sets,” Int. J. Inf. Technol. Decis. Mak., vol. 14, no. 3, pp. 621–657, 2015.

[85] Ü. Şengül, M. Eren, S. Eslamian Shiraz, V. Gezder, and A. B. Sengül, “Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey,” Renew. Energy, vol. 75, pp. 617–625, 2015.

[86] A. Memari, A. Dargi, M. R. Akbari Jokar, R. Ahmad, and A. R. Abdul Rahim, “Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method,” J. Manuf. Syst., vol. 50, no. September 2018, pp. 9–24, 2019.

[87] N. Mao, M. Song, and S. Deng, “Application of TOPSIS method in evaluating the effects of supply vane angle of a task/ambient air conditioning system on energy utilization and thermal comfort,” Appl. Energy, vol. 180, pp. 536–545, 2016.

[88] K. Rudnik and D. Kacprzak, “Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system,” Appl. Soft Comput. J., vol. 52, pp. 1020–1041, 2017.

[89] P. Wang, Z. Zhu, and Y. Wang, “A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design,” Inf. Sci. (Ny)., vol. 345, pp. 27–45, 2016.

[90] P. Chithambaranathan, N. Subramanian, A. Gunasekaran, and P. K. Palaniappan, “Service supply chain environmental performance evaluation using grey based hybrid MCDM approach,” Int. J. Prod. Econ., vol. 166, pp. 163–176, 2015.

[91] J. Y. Tsai, J. F. Ding, G. S. Liang, and K. D. Ye, “Use of a hybrid mcdm method to evaluate key solutions influencing service quality at a port logistics center in Taiwan,” Brodogradnja, vol. 69, no. 1, pp. 89–105, 2018.

[92] M. Yazdani, S. Hashemkhani Zolfani, and E. K. Zavadskas, “New integration of MCDM methods and QFD in the selection of green suppliers,” J. Bus. Econ. Manag., vol. 17, no. 6, pp. 1097–1113, 2016.

[93] I. Emovon, R. A. Norman, and A. J. Murphy, “Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems,” J. Intell. Manuf., vol. 29, no. 3, pp. 519–531, 2018.

[94] S. Vinodh, T. S. Sai Balagi, and A. Patil, “A hybrid MCDM approach for agile concept selection using fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS,” Int. J. Adv. Manuf. Technol., vol. 83, no. 9–12, pp. 1979–1987, 2016.

[95] A. T. I. and E. A. A. Faculty, “A new integrated decision making approach based on SWARA and OCRA methods for the hotel

2661



selection problem,” Int. J. Adv. Oper. Manag., vol. 8, no. 2, pp. 140–151, 2016. [96] B. JUODAGALVIENĖ, Z. TURSKIS, J. ŠAPARAUSKAS, and A. ENDRIUKAITYTĖ, “Integrated Multi-Criteria Evaluation

of House’S Plan Shape Based on the Edas and Swara Methods,” Eng. Struct. Technol., vol. 9, no. 3, pp. 117–125, 2017. [97] J. Heidary Dahooie, E. Beheshti Jazan Abadi, A. S. Vanaki, and H. R. Firoozfar, “Competency-based IT personnel selection

using a hybrid SWARA and ARAS-G methodology,” Hum. Factors Ergon. Manuf., vol. 28, no. 1, pp. 5–16, 2018. [98] X. Zhang, Y. Deng, F. T. S. Chan, A. Adamatzky, and S. Mahadevan, “Supplier selection based on evidence theory and analytic

network process,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 230, no. 3, pp. 562–573, 2016. [99] M. Nilashi, H. Ahmadi, A. Ahani, R. Ravangard, and O. bin Ibrahim, “Determining the importance of Hospital Information

System adoption factors using Fuzzy Analytic Network Process (ANP),” Technol. Forecast. Soc. Change, vol. 111, no. 2016, pp. 244–264, 2016.

[100] L. Wang, H. yu Zhang, J. qiang Wang, and L. Li, “Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project,” Appl. Soft Comput. J., vol. 64, pp. 216–226, 2018.

[101] J. Hu, X. Zhang, Y. Yang, Y. Liu, and X. Chen, “New doctors ranking system based on VIKOR method,” Int. Trans. Oper. Res., vol. 27, no. 2, pp. 1236–1261, 2020.

[102] F. Lolli et al., “On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application,” Expert Syst. Appl., vol. 120, pp. 217–227, 2019.

[103] L. T. Sianturi, A. Karim, A. Putera, and U. Siahaan, “Best Student Selection Using Extended Promethee II Method,” Int. J. Recent Trends Eng. Res., vol. 3, no. 8, pp. 21–29, 2017.

[104] M. Gul, E. Celik, A. T. Gumus, and A. F. Guneri, “A fuzzy logic based PROMETHEE method for material selection problems,” Beni-Suef Univ. J. Basic Appl. Sci., vol. 7, no. 1, pp. 68–79, 2018.

[105] D. Schitea, M. Deveci, M. Iordache, K. Bilgili, İ. Z. Akyurt, and I. Iordache, “Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS,” Int. J. Hydrogen Energy, vol. 44, no. 16, pp. 8585–8600, 2019.

.

2662



Biographies

Fatma Eltarabishi is a full-time student of Engineering Management at University of Sharjah, UAE and earned a degree in Bachelor of Science in Nuclear Engineering from University of Sharjah, UAE. Research Interest: Multi-Criteria Decision Making (MCDM), Engineering Management, Safety Engineering Management and Radiation protection. Omar Hassan Omar is a Laboratory Engineer in the Department of Industrial Engineering and Engineering Management at the University of Sharjah and holder of a degree in Bachelor of Science in Industrial Engineering and Engineering Management. In addition, a current student in the Master of Science in Engineering Management program at the University of Sharjah. Research interests include: Multi-Criteria Decision Making (MCDM), Engineering Management, Safety Engineering Management and Tribology and Slip, trip & fall.” Imad Alsyouf is an associate professor of Industrial Engineering, employed by University of Sharjah, UAE. He is the founder and coordinator of the Sustainable Engineering Asset Management (SEAM) Research Group. He has produced more than 67 conference and journal papers. He has about 30 years of industrial and academic experience in various positions in Jordan, Sweden and UAE. His research interests include reliability, quality, maintenance, and optimization. He has developed and taught more than 25 post and undergrad courses. He delivered training courses in Kaizen, TQM, and organizational excellence. Maamar Bettayeb received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from University of Southern California, Los Angeles, in 1976, 1978 and 1981, respectively. He worked as a Research Scientist at the Bellaire Research Center at Shell Oil Development Company, Houston, Texas, USA. From 1982 to 1988, He directed the Instrumentation and Control Laboratory of High Commission for Research in Algeria. In 1988, He joined the Electrical Engineering Department at King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. He has been Professor at University of Sharjah UAE since August 2000. He is the Vice Chancellor for Research and Graduate Studies at University of Sharjah, starting September 2014. He has published over 350 journal and conference papers in the fields of control and signal processing. He has also supervised over 50 M. Sc. and Ph. D. students. His recent research interest is in process control, fractional dynamics and control, soft computing, and renewable energies.

2663

multi-criteria decision making methods and their

Documents