Combination of Fuzzy TOPSIS and Fuzzy Ranking for Multi Attribute Decision Making

Mehregan, Mohammad Reza; Safari, Hossein

doi:10.1007/11785231_28

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…Then, the result of computing the intervals of each option from the fuzzy positive ideal and the fuzzy negative ideal was calculated with the CC of each component. Accordingly, all these 38 components were ranked in the descending order of CC scores, and each component with larger CC was considered a component with a better rating, and finally, the top components were identified and introduced [27,119] (Table 4).…”

Section: Resultsmentioning

confidence: 99%

“…Ranking the components in order of coefficient of closeness and selection of the top components: In this step, the distance of each component to the ideals as well as the coefficient of closeness ( CC ) was calculated based on relation no. 17 introduced by Mehregan and Safari [27], Hsu and Chen [136] and Cheng [137]. Finally, at this stage, the components were ranked based on CC .…”

Section: Methodsmentioning

confidence: 99%

“…For the use of the fuzzy TOPSIS technique, there are two different approaches: first, component-based evaluation (ranking n components based on m components using k expert), and second, evaluation based on experts' viewpoints (ranking n components using k experts' opinions) [27]. In this study, the second approach (ranking of n components using k experts' opinions) was used to rank the content generation components.…”

Section: Analysis Using Fuzzy Topsismentioning

confidence: 99%

“…Studies show that many decision-making researches have used fuzzy TOPSIS to rank and evaluate multi-factors and multi-components because its main application is in decision-making studies [24][25][26][27][28]. Furthermore, other fields use fuzzy TOPSIS to make better decisions when they need to rank and evaluate multiple components.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating and ranking the digital content generation components for marketing the libraries and information centres’ goods and services using fuzzy TOPSIS technique

Naseri

Chakoli

Malekolkalami

2021

Journal of Information Science

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Since content audiences, including libraries and information centres, are increasingly geared to digital environments and virtual networks, the production and delivery of high-quality digital content are becoming continuously important. So far, several components have been introduced by researchers for evaluating the quality of digital content generation. However, due to the uncertainty of the importance rate and value of each of these components, it has not yet been possible to use them effectively to evaluate the content produced. This study aimed to rank the components of content generation to allow accurate evaluation of them for users as well as content providers and distributors including libraries and marketers. The ranked content can motivate digital content producers and distributors to better evaluate the quality of digital content, better attract customers and make more effective decisions about the quality of digital content use based on their specific goals. Initially, 42 of the most important components were identified from the literature. Then, the next steps were taken to rank these components, and based on three rounds of Delphi interviews, the experts’ views on the importance rate of each of the components were obtained, analysed and ranked. Since in this ranking, the importance of a wide range of components should be highlighted towards each other, the fuzzy TOPSIS technique was emphasised for analysing the views of 16 experts in the field of content generation in Iran. This ranking indicated that components such as ‘findable and access’, ‘non-disturbing and helpful’, ‘clear’ and ‘remarkable’ are the main pillars of content generation and are of the utmost importance. The results can be used as an effective tool to improve the quality of content. Moreover, it increases audience engagement in digital environments and social networks, and encourages them to make more use of the digital content of libraries.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Analysis Using Fuzzy Topsismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating and ranking the digital content generation components for marketing the libraries and information centres’ goods and services using fuzzy TOPSIS technique

Naseri

Chakoli

Malekolkalami

2021

Journal of Information Science

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“…The following gives a brief description of basic procedures for the fuzzy evaluation method [11][12][13]:…”

Section: Fuzzy Evaluation Methodsmentioning

confidence: 99%

Application of integrated robust designation methodology in multi-objective optimization

Wang

Chen

2012

Journal of Information and Optimization Sciences

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This paper demonstrates the optimizing process of multiple quality characteristics for Wire Electrical Discharge Machining (WEDM) of Fe-Al alloy parts via the Taguchi method-based Gray analysis. The modifi ed algorithm adopted here successfully detrains the optimum settings of machine parameters and combines multiple quality characteristics into one integrated numerical value called the Gray relational grade. The essential machining parameters are identifi ed as (1) servo voltage, (2) pulse-on time, (3) pulse-off time, (4) wire tension, (5) wire feed rate, and (6) no load voltage of the primary WEDM. Multiple quality characteristics required include: (1) high material removal rate, (2) small groove width, and (3) low surface roughness following WEDM. This work machines Fe-Al alloy parts under controlled machine parameter settings, and measures the above quality characteristics. The optimized machine parameter settings clearly improve quality characteristics of the machined workpiece compared to quality levels achieved for initial machine parameter settings. Material removal rate changes from 1.63 to 4.26 mm 2 /min; groove width decreases from 0.30 to 0.29 mm, and surface roughness on the workpiece reduces from 1.26 to1.21 nmRa.

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A New Fuzzy MADM Approach and its Application to Project Selection Problem

Pahlavani¹

2010

International Journal of Computational Intelligence Systems

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In this paper, a novel fuzzy MADM model with some specifications that make it distinguished from the available methods. Decision matrix is defined as a full fuzzy structure. The model only uses information on the alternatives i.e. does not require pre-assigned weight values for the attributes. The weights will be achieved through a matching mechanism between the obtainable alternatives' preferences and a fuzzy preference relation of the alternatives stated by decision makers. This is applied using a mathematical programming model. Since the resulted model is non-linear and hard to solve using the classic optimization methods, Simulated Annealing is proposed to find optimum or near optimum weights. Having the weights of attributes, the alternatives' ranking is determined using the statistical measures of their fuzzy values. The model is applied on the project selection problem to study its efficiency and applicability to MADM problems.

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Combination of Fuzzy TOPSIS and Fuzzy Ranking for Multi Attribute Decision Making

Cited by 4 publications

References 17 publications

Evaluating and ranking the digital content generation components for marketing the libraries and information centres’ goods and services using fuzzy TOPSIS technique

Evaluating and ranking the digital content generation components for marketing the libraries and information centres’ goods and services using fuzzy TOPSIS technique

Application of integrated robust designation methodology in multi-objective optimization

A New Fuzzy MADM Approach and its Application to Project Selection Problem

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