A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy

Rashidi, Kamran; Cullinane, Kevin

doi:10.1016/j.eswa.2018.12.025

Cited by 188 publications

(96 citation statements)

References 92 publications

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“…Similarly, the subject of supplier selection has also been frequently considered by researchers. For instance, Memari et al [50], Rashidi and Cullinane [51], and Bera et al [52] aimed to find the best supplier using the fuzzy TOPSIS methodology. However, it was also identified that the number of studies using IT2 fuzzy TOPSIS are very limited in the literature.…”

Section: Literature Review On Methodologymentioning

confidence: 99%

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Qiu¹,

et al. 2020

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This study aimed to analyze the systematic risks of wind energy investments. Within this framework, E7 countries are included in the scope of the examination. A large literature review was carried out and 12 different systematic risk factors that could exist in wind energy investments were identified. The analysis process of the study consisted of two different stages. First, the specified risk criteria were weighted with the help of the interval type 2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Second, E7 countries were ranked according to the risk management effectiveness in wind energy investments. In this process, the IT2 fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was taken into consideration. The findings show that volatility in exchange rates and interest rates were the most important risks in wind energy investments. In addition, it was determined that China and Indonesia were the most successful countries in managing risks in wind energy investments. In contrast, India, Russia, and Turkey were determined to be the least successful. Additionally, the IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method was applied as a robustness check of the extended VIKOR method. It was concluded that the ranking results of the IT2 fuzzy TOPSIS method were similar to the results of the IT2 fuzzy VIKOR. It can be understood that the proposed ranking method was consistent with the comparative analysis results. From this point of view, it was observed that countries should take measures regarding their exchange rate and interest rate risks in order to increase the efficiency in wind energy investments. In this context, companies should first ensure that they do not have a foreign exchange short position in their balance sheets by conducting an effective financial analysis. In addition, it is important to use financial derivatives to minimize the exchange rate and interest rate risks. Using these results, it will be possible to manage this risk by taking the reverse position for the existing foreign currency and interest risk. In this way, it will be possible to increase the efficiency of wind energy investments, which will contribute to the social and economic development of each respective country.Energies 2020, 13, 1423 2 of 25 their own energy resources can benefit from these resources [1]. This situation gives these countries a serious cost advantage. On the other hand, countries that do not have sufficient energy resources have to supply the energy they need from outside, which makes these countries dependent on energy. Because of this problem, countries are developing several strategies to produce their own energy [2].Renewable energy sources are an important alternative that allows countries to produce their own energy. The most important feature of these energy alternatives, which come in different forms, such as wind, solar, biomass, geothermal, and hydroelectricity, is that they obtain their resources from natur...

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Section: Literature Review On Methodologymentioning

confidence: 99%

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Qiu¹,

et al. 2020

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“…Step 4: a nonlinear min-max mathematical programming problem is constructed as model (13), which can be transferred to the model (14), and the optimal Mathematical Problems in Engineering 3 weights (w * 1 , w * 2 , . .…”

Section: Best-worstmentioning

confidence: 99%

“…Compared with standard pairwise comparison methods such as the AHP, the BWM uses only integer numbers in describing preferences, reduces the times of comparisons, and most importantly provides more consistent and reliable results [24]. Due to the inconsistency of evaluation vectors and the nonlinearity of programming model (13), there may be multiple optimal solutions in some cases [35]. In order to obtain a unique solution, Rezaei improved the mathematical programming by transforming the objective function to the set {|w B − a Bj w j |, |w j − a jW w W |}.…”

Section: Best-worstmentioning

confidence: 99%

“…e increase of imprecision and complexity in realworld problems leads to the fact that decision-makers might be unable to express personal preferences with numerical values, so some theories dealing with imprecision are introduced into MCGDM problems, especially the theory of fuzzy sets developed by Zadeh [12]. Classical methods have been extended to solve uncertain MCGDM problems based on fuzzy sets as well as their generations, such as fuzzy TOPSIS [13], triangular fuzzy AHP [14], intuitionistic fuzzy VIKOR [15], fuzzy prospect theory [16], intuitionistic fuzzy ELECTRE [17], and Pythagorean fuzzy PROMETHEE [18].…”

Section: Introductionmentioning

confidence: 99%

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Rough-Number-Based Multiple-Criteria Group Decision-Making Method by Combining the BWM and Prospect Theory

Jia

Wang

2020

Mathematical Problems in Engineering

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Multicriteria group decision-making (MCGDM) problems have been a research hotspot in recent years, and prospect theory is introduced to cope with the risk and imprecision in the process of decision-making. To guarantee the effectiveness of information aggregation and extend the feasibility of prospect theory, this paper proposes a novel decision-making approach based on rough numbers and prospect theory to solve risky and uncertain MCGDM problems. Firstly by combining rough numbers and the bestworst method (BWM), we construct a linear programming model to calculate rough criteria weights, which are defined by lower limitations and upper limitations. en for the imprecision of value function and weighting function in prospect theory, we propose a novel method with the aid of combining rough numbers and prospect theory to handle the risk in decision-making problems. Finally, a numerical example involving investment is introduced to illustrate the application and validity of the proposed method.

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“…FTOPSIS method also known as a method that easy to handle intangible and less cumbersome mathematical calculations. This method has been explored by many researchers such as [4] combined FTOPSIS with Fuzzy Data Development Analysis (DEA). In [5] integrated FTOPSIS and Interval-Valued Intuitionistic Fuzzy Coginitve Maps (IVIFCMs) for model the supplier selection task.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy TOPSIS with Z-Number and Alpha-Cut in Identifying the Key Factor of Smoking Behaviours in Jordan

2019

ijrte

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The most famous method when dealing with decision making is Multi-Criteria Decision Making (MCDM). One of the easiest technique from MCDM is Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). However, there are some circumstances in FTOPSIS still needs to be enhanced. Therefore, this paper concentrates on: 1) to develop FTOPSIS with Z-number and α -cut based MCDM problem; the combination of Z-Number and α-cut is seen suitable to solve uncertainty issue due to the additional restriction and reliability parts; 2) to validate the propose method for solving smoking problems in Jordan; three decision makers which are parent, student and health organization are requested to answer all the questionnaires based on smoking behaviour problems. The study presents six steps of FTOPSIS with Z-numbers and αcut to verify the position of the smoking behaviour; 3) to evaluate the efficiency of the proposed method using 10 different number of α-cut. Ten different numbers are 0.1 till 0.9. Results illustrate that many Jordanian smoke because they want to lose their weight. From the study, this proposed method opens a new revolution in MCDM area to provide different ways when dealing with decision making problems. Thus, give us variety of techniques in decision making area.

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A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy

Cited by 188 publications

References 92 publications

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets

Rough-Number-Based Multiple-Criteria Group Decision-Making Method by Combining the BWM and Prospect Theory

A Fuzzy TOPSIS with Z-Number and Alpha-Cut in Identifying the Key Factor of Smoking Behaviours in Jordan

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