An integrating OWA–TOPSIS framework in intuitionistic fuzzy settings for multiple attribute decision making

Wang, Tianri; Liu, Juan; Li, Jizu; Chong-huai, Niu

doi:10.1016/j.cie.2016.05.029

Cited by 47 publications

(20 citation statements)

References 31 publications

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“…TOPSIS method, which was developed by Hwang and Yoon [51], is a broadly used multi-criteria method for improving the decision-making process. By using TOPSIS method, the decision-maker solves selection/evaluation problem because it is based on a sound logic, which represents the rational of human choice [52].…”

Section: Characteristic Variablementioning

confidence: 99%

Benchmarking Internet Promotion of Renewable Energy Enterprises: Is Sustainability Present?

Andreopoulou

Koliouska

2018

Sustainability

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Sustainability constitutes a broad discipline that focuses on the social, economic and environmental impact of human activities. Many policies and strategies have been developed for the pursuit of environmental sustainability and the guidance to a green society. Many enterprises have taken meaningful steps to improve their own environmental performance through corporate sustainability and environmental management. Environmental management contributes to significant improvements to environmental performance of the enterprises. This paper aims to evaluate the Renewable Energy Enterprises performance in the Internet in Thessaloniki Prefecture regarding the characteristics of sustainability using Multi-criteria Decision Analysis. TOPSIS method was used to provide a ranking of the Renewable Energy Enterprises according to their sustainability and finally conclude to a benchmark. According to the results of the research, the Renewable Energy Enterprises achieve a good level of sustainability but not the optimum. However, the entrepreneurs should adopt modern environmental policy, sustainable marketing, green network framework and certified environmental management system in order to consider their enterprise sustainable.

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Section: Characteristic Variablementioning

confidence: 99%

Benchmarking Internet Promotion of Renewable Energy Enterprises: Is Sustainability Present?

Andreopoulou

Koliouska

2018

Sustainability

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“…The most suitable alternative should be closest to the positive ideal solution and the farthest from the negative ideal solution. At present, with the deepening realization about TOPSIS, some extended TOPSIS techniques have been used widely for MADM problems to overcome the vagueness in the judgments made by the assessors [11][12][13][14][15][16][17][18][19][20][21]. In order to overcome the problem of index weight calculation, AHP is the main auxiliary method of TOPSIS, and fuzzy sets are introduced together to facilitate the quantification of the two pairs of indicators [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome the problem of index weight calculation, AHP is the main auxiliary method of TOPSIS, and fuzzy sets are introduced together to facilitate the quantification of the two pairs of indicators [11][12][13]. Some researchers have improved TOPSIS by using the intuitionistic fuzzy number, triangular fuzzy number, vague set, intuitionistic fuzzy entropy, and other uncertainty tools, which can solve MADM problems with intuitionistic fuzzy environment or linguistic fuzziness [7,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Novel TOPSIS Method Based on Improved Grey Relational Analysis for Multiattribute Decision‐Making Problem

Wei-guo

2019

Mathematical Problems in Engineering

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Multiattribute decision-making (MADM) problem is difficult to assess because of the large number of attribute indices and the diversity of data distribution. Based on the understanding of data dispersion degree, a new grey TOPSIS method for MADM is studied. The main idea of this paper is to redefine the grey relational analysis through the dispersion of data distribution and redesign the TOPSIS by using the improved grey relational analysis. As a classical multiattribute decision analysis method, traditional TOPSIS does not consider the data distribution of the degree of dispersion and aggregation when it is compared with the optimal and worst alternative solutions. In view of the limitations of traditional TOPSIS, this paper has made two major improvements to TOPSIS. Firstly, the new grey relational analysis is applied to evaluate the grey positive relational degree between each alternative and the optimal solution and compute the grey negative relational degree between each alternative and the worst solution. Secondly, the weights of every attribute index about the optimal and worst solutions are put forward based upon the distance standard deviation and the average distance. Finally, the comprehensive grey TOPSIS is utilized to analyze the ranking of weapon selection problem. The numerical results verify the feasibility of the improved grey relational analysis and also highlight the practicability of the grey comprehensive TOPSIS.

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“…In existing studies, many different methods have been developed for multiple attribute decision analysis (MADA). Representative methods include multiple attribute utility function (MAUF) methods (Balla et al 2014;Keeney and Raiffa 1993;Butler et al 1997;Butler et al 2001;Wakker et al 2004), multiple attribute value function methods (Belton and Stewart 2002;Chin et al 2015;Fischer 1995;Kadziński et al 2014;Keeney 2002;Lan et al 2015;Zhang et al 2017), outranking methods such as PROMETHEE methods (Chen 2014a;Miłosz and Krzysztof 2016) and ELECTRE methods (Chen 2014b;Corrente et al 2016), and distance based methods such as the extensions of TOPSIS method (Baykasoğlu and Gölcük 2015;Wang et al 2016) and VIKOR method (Qin et al 2015;Madjid et al 2016). One similarity among those different methods is that attribute weights are taken into account although the meanings of the weights may be different.…”

Section: Introductionmentioning

confidence: 99%

Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions

Xue

2018

Knowledge-Based Systems

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Attribute weight assignment plays an important role in multiple attribute decision analysis (MADA). When the performances of alternatives on each attribute are expressed by distributions instead of single values, how to use the differences in the performances to obtain attribute weights is an interesting but difficult issue. To address this issue, in this paper, we propose a method for obtaining attribute weights from discriminating power in belief distributions. With the consideration of the differences among the utilities of all assessment grades used to profile belief distributions, the dissimilarity based discriminating power, the standard deviation based discriminating power, and the Gini's mean difference based discriminating power of the performances of all alternatives on each attribute are constructed to determine three sets of respective weights of attributes. They are convexly combined using three coefficients to generate integrated weights of attributes. To relieve the burden on a decision maker to provide precise values for the three coefficients, they are allowed to change between 0 and 1, as long as their sum is equal to 1. Under such constraints on the three coefficients, an optimization model is constructed to determine the minimum and maximum expected utilities of each alternative. From the expected utilities, all alternatives are then compared using Hurwicz rule with the provided optimism degree interval to generate solutions to MADA problems. The transitivity of the comparison outcomes among three alternatives under a given optimism degree interval is theoretically analyzed to guarantee the rationality of the outcomes. A focal form selection problem is investigated to demonstrate the applicability and validity of the proposed method.

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An integrating OWA–TOPSIS framework in intuitionistic fuzzy settings for multiple attribute decision making

Cited by 47 publications

References 31 publications

Benchmarking Internet Promotion of Renewable Energy Enterprises: Is Sustainability Present?

Benchmarking Internet Promotion of Renewable Energy Enterprises: Is Sustainability Present?

A Novel TOPSIS Method Based on Improved Grey Relational Analysis for Multiattribute Decision‐Making Problem

Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions

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