Extended EDAS Methods for Multi-Criteria Group Decision-Making Based on IV-CFSWAA and IV-CFSWGA Operators With Interval-Valued Complex Fuzzy Soft Information

Jian, Fan; Rui, Cheng; Wu, Meiqin

doi:10.1109/access.2019.2932267

Cited by 19 publications

(15 citation statements)

References 36 publications

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“…EDAS is a relatively new method compared with the other multi-criteria decision-making methods. This method includes positive and negative distances from the average solutions (Fan et al, 2019). The motivation for employing the EDAS method for the current research is that the obtained results are based on the average solution that represents normalised data that significantly limit the chances of deviation from the best solution, which allows this technique to produce more accurate solutions in problemsolving (Tadić et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Circular economy as assistance for sustainable development in OECD countries

Skvarciany

Lapinskaitė

Volskytė

2021

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Research background: Circular economy is of great importance, as it plays a vital role in ensuring the reuse of waste created and, therefore, reduces the waste of limited resources, which is the primary goal of the general economic concept. In line with the circular economy, sustainable development gains great attention, as the United Nations announced the sustainable development goals that should be reached by 2030. Hence, the current paper aims at examining whether the circular economy could be treated as an effective assistance tool for sustainable development of OECD countries. Purpose of the article: The paper aims to investigate whether the circular economy could serve as an assistance tool for sustainable development and, therefore, seeks to determine if the circular economy could directly impact a country?s sustainable development. Methods: First, the countries chosen were prioritised using the Analytic Hierarchy Process (AHP) and the Evaluation Based on Distance from Average Solution (EDAS) methodologies. AHP method was used for weight assignment to the circular economy indicators that were further used for OECD countries? prioritisation procedure for which multi-criteria decision-making method EDAS was employed. Second, to reveal a link between the circular economy ranking results and sustainable development, a comparative analysis was done. Third, the impact of the country?s circular economy on sustainable development was evaluated using the fixed-effect regression model on four years of panel data from 2016 to 2019 for the sample of 32 OECD countries. Findings & value-added: The comparative analysis of the circular economy?s prioritisation results and Sustainable Development Goals Index (SDGI) ranking showed 20 out of 32 matches, assuming a link between the circular economy and sustainable development could be made. The fixed-effect regression equation results demonstrate that the unemployment rate, poverty rate, air pollution exposure, and CO2 emission per capita negatively influence sustainable development. In contrast, indicators such as gross domestic expenditure on R&D, renewable energy, number of passenger cars in use, and households with Internet access positively impact SDGI. The hypothesis that the circular economy is seen as an assistance for sustainable development and directly affects a country?s sustainability was approved. The paper contributes to the scientific literature in the field of circular economy and sustainable development interaction and could be seen as an assumption for new research directions, focusing on the linkage between circular economy and sustainable development. Moreover, the obtained results could contribute to a country?s policy-makers by highlighting the essential indicators of a circular economy that should be considered while forming the strategy of a country?s sustainable development.

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Section: Methodsmentioning

confidence: 99%

Circular economy as assistance for sustainable development in OECD countries

Skvarciany

Lapinskaitė

Volskytė

2021

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“…Several extensions are also proposed for the EDAS method, such as: a fuzzy EDAS [19], an interval type-2 fuzzy extension of the EDAS method [18], a rough EDAS [20], Grey EDAS [28], intuitionistic fuzzy EDAS [29], interval-valued fuzzy EDAS [30], an extension of EDAS method in Minkowski space [23], an extension of the EDAS method under qrung orthopair fuzzy environment [31], an extension of the EDAS method based on interval-valued complex fuzzy soft weighted arithmetic averaging (IV-CFSWAA) operator and the interval-valued complex fuzzy soft weighted geometric averaging (IV-CFSWGA) operator with interval-valued complex fuzzy soft information [32], and an extension of the EDAS equipped with trapezoidal bipolar fuzzy information [33].…”

Section: Introductionmentioning

confidence: 99%

A Single-Valued Neutrosophic Extension of the EDAS Method

Stanujkić

Karabašević

Popović

et al. 2021

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This manuscript aims to propose a new extension of the EDAS method, adapted for usage with single-valued neutrosophic numbers. By using single-valued neutrosophic numbers, the EDAS method can be more efficient for solving complex problems whose solution requires assessment and prediction, because truth- and falsity-membership functions can be used for expressing the level of satisfaction and dissatisfaction about an attitude. In addition, the indeterminacy-membership function can be used to point out the reliability of the information given with truth- and falsity-membership functions. Thus, the proposed extension of the EDAS method allows the use of a smaller number of complex evaluation criteria. The suitability and applicability of the proposed approach are presented through three illustrative examples.

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“…Recently, with the boost up of various decision-making problems inspired by time variants or phase changes, an extension of the Fuzzy Set namely the Complex Fuzzy Set (CFS) [37] with new membership functions including both the amplitude and the phase terms has been proposed. CFS has been applied with much concentration by new fuzzy aggregation operators, complex fuzzy soft information, distance measures, and complex fuzzy concept lattice [38][39][40][41][42][43][44]. The advantage of CFS is the capability to model phenomena and events by the phase term to show their overall progress within a given context.…”

Section: Introductionmentioning

confidence: 99%

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

et al. 2020

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Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of

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Extended EDAS Methods for Multi-Criteria Group Decision-Making Based on IV-CFSWAA and IV-CFSWGA Operators With Interval-Valued Complex Fuzzy Soft Information

Cited by 19 publications

References 36 publications

Circular economy as assistance for sustainable development in OECD countries

Circular economy as assistance for sustainable development in OECD countries

A Single-Valued Neutrosophic Extension of the EDAS Method

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

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