Fuzzy Analytic Hierarchy Process-Based Multi-Criteria Decision Making for Universities Ranking

Aliyev, Rashad R; Temizkan, Hasan; Aliyev, Rafig R.

doi:10.3390/sym12081351

Cited by 34 publications

(28 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prestigious rankings such as Academic Ranking of World Universities (ARWU); Times Higher Education World University Ranking (THE); Quacquarelli Symonds (QS); THE Impact; Sustainability Tracking, Assessment and Rating System (STARS) or IU GreenMetric World University Ranking do not provide enough information about their methodology to obtain the weightings of the different indicators and criteria [5,23]. In cases where the methodology employed to obtain the weights is explained, expert opinion and AHP methodology are generally used, as in the proposals of some researchers [6,20,23,35,54]. When experts are asked, the weights depend on the selection of the experts and is subject to their subjectivity, which introduces a bias in the ranking tables.…”

Section: Methodsmentioning

confidence: 99%

Ranking the Performance of Universities: The Role of Sustainability

et al. 2021

View full text Add to dashboard Cite

University rankings assess the performance of universities in various fields and aggregate that performance into a single value. In this way, the aggregate performance of universities can be easily compared. The importance of rankings is evident, as they often guide the policy of Higher Education Institutions. The most prestigious multi-criteria rankings use indicators related to teaching and research. However, many stakeholders are now demanding a greater commitment to sustainable development from universities, and it is therefore necessary to include sustainability criteria in university rankings. The development of multi-criteria rankings is subject to numerous criticisms, including the subjectivity of the decision makers when assigning weights to the criteria. In this paper we propose a methodology based on goal programming that allows objective, transparent and reproducible weighting of the criteria. Moreover, it avoids the problems associated with the existence of correlated criteria. The methodology is applied to a sample of 718 universities, using 11 criteria obtained from two prestigious university rankings covering sustainability, teaching and research. A sensitivity analysis is carried out to assess the robustness of the results obtained. This analysis shows how the weights of the criteria and the universities’ rank change depending on the λ parameter of the goal programming model, which is the only parameter set by the decision maker.

show abstract

Section: Methodsmentioning

confidence: 99%

Ranking the Performance of Universities: The Role of Sustainability

et al. 2021

View full text Add to dashboard Cite

show abstract

“…If the intersection and the union are algebraically represented by the product and the algebraic sum, respectively, to represent a closer representation of human aggregation procedures, then (15) becomes:…”

Section: Compensatory and Operatormentioning

confidence: 99%

“…Ref. [15] proposed a fuzzy AHP for ranking the performances of five UK universities with respect to 4 criteria. In Ref.…”

Section: Introductionmentioning

confidence: 99%

A Decision-Making Approach for Ranking Tertiary Institutions’ Service Quality Using Fuzzy MCDM and Extended HiEdQUAL Model

Oladipupo

Amoo

Daramola

2021

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

The attainment of excellence in institutions is maintained through the institutions’ adherence to its core values and efficient service delivery. These factors are very important in facilitating global development of a country and determining the world ranking of an institution. To this effect, this study presents an effective approach for evaluating and ranking quality of services in a higher institution, taking four higher institutions in Nigeria as case studies. Service quality consists of different attributes and many of them are intangible and difficult to measure, which means that using the previously known measurement approach will be insufficient. Therefore, a fuzzy method was proposed to resolve the ambiguity of the concepts and intra-uncertainty, which are associated with human judgments in decision-making. This study adopted a contextualized service quality model for educational domain called HiEdQUAL with some extended criteria in order to evaluate the perception of service quality by respondents from the selected higher institutions: two private universities and two public universities from the south-west region of Nigeria. Four Multi-Criteria Decision-Making (MCDM) methods: TOPSIS, Yager’s min-max, Compensatory AND, and Ordered Weighted Averaging are applied to comparatively evaluate the quality of services in the four higher institutions. The MCDM methods are engaged independently to validate the reliability of the ranking results. The importance weight of each performance criterion is found with Fuzzy Analytical Hierarchy Process (FAHP) algorithm. This study has been able to practically establish Ext-HiEdQUAL as a new service quality model for higher education with six concepts and 33 criteria. The output of the Fuzzy MCDM ranking recommends institution B as the best institution to students based on the Ext-HiEdQUAL measures. Also, findings from the sensitivity analysis showed that Yager’s min-max outperform the other investigated methods in this study by being consistent and exceptionally tolerant in most instances when there is significant deviation in criteria weights.

show abstract

“…en compare the relative importance of the indicators of each evaluation level of the model with the indicators of the previous level to determine the weight of the influencing factors of each level. is paper uses the judgment matrix to achieve this goal, that is, to evaluate the importance of a certain criterion level index defined in the cable insulation aging life assessment model to the target level index that needs to be analyzed and evaluated [24,25]. is hierarchical model of message evaluation is to compare the importance of indicators at the criterion level and the target level.…”

Section: Determine the Evaluation Index System Of Cable Insulation Lifementioning

confidence: 99%

Evaluation Model of Cable Insulation Life Based on Improved Fuzzy Analytic Hierarchy Process

Xian

Guo

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Since the human society entered the electrification era, people’s work, life, and even the production and development of various industries in society are inseparable from the supply of electricity. The purpose of this paper is to establish a cable insulation life evaluation model based on the improved fuzzy analytic hierarchy process to test the current insulation characteristics and life of various types of cables, so as to ensure the quality of power supply and safe production of power. This paper first understands the cable insulation characteristics test process and electrical power-related knowledge through a large number of literature studies and consultation with power grid professionals. Then this paper combines the theoretical research and improvement of the fuzzy analytic hierarchy process based on the actual situation of the cable insulation characteristics, thereby constructing the cable insulation life evaluation model. In this evaluation model, this paper combines the fuzzy comprehensive evaluation method and the analytic hierarchy process as well as the actual situation of the cable characteristic test to analyze and predict the insulation life of the cable. Finally, the linear test and reliability estimation of the experimental results are carried out, and the application of this evaluation model is extended to the evaluation of insulation life of other types of cables. Based on this, this paper proposes a cable insulation life evaluation model based on the improved fuzzy analytic hierarchy process. This model combines the Weibull model and the Arrhenius model commonly used in the assessment of cable insulation aging life and the improved fuzzy analytic hierarchy process. Experiments have proved that, within a certain range, temperature factors have a significant impact on the cable insulation life. For example, when the temperature is below 55°C, the insulation life of the cable is usually 30 to 50 years. However, the evaluation model of the improved fuzzy analytic hierarchy process in this paper has much higher accuracy in evaluating cable insulation life than other life evaluation methods.

show abstract

Fuzzy Analytic Hierarchy Process-Based Multi-Criteria Decision Making for Universities Ranking

Cited by 34 publications

References 20 publications

Ranking the Performance of Universities: The Role of Sustainability

Ranking the Performance of Universities: The Role of Sustainability

A Decision-Making Approach for Ranking Tertiary Institutions’ Service Quality Using Fuzzy MCDM and Extended HiEdQUAL Model

Evaluation Model of Cable Insulation Life Based on Improved Fuzzy Analytic Hierarchy Process

Contact Info

Product

Resources

About