scholarly journals Comprehensive Assessment of Distance Learning Modules by Fuzzy AHP-TOPSIS Method

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 409
Author(s):  
Svajone Bekesiene ◽  
Aidas Vasilis Vasiliauskas ◽  
Šárka Hošková-Mayerová ◽  
Virgilija Vasilienė-Vasiliauskienė
Keyword(s):  
Decision Making ◽  
Distance Learning ◽  
Quality Assessment ◽  
Evaluation Method ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Evaluation Methodology ◽  
Practical Implementation ◽  
Ideal Solution ◽  
Analysis Method

This survey is focussed on distance learning studies, where there can be met a lot of technical obstacles, which creates complications in decision making. To get an ideal solution for these kinds of problems, the Fuzzy TOPSIS (Technique for Order Preference by Similarities to Ideal Solution) is one of the best solutions. Therefore, this paper presents the distance learning quality assessment surveys when the Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS methods are used. Research results describe the application of the Fuzzy AHP—TOPSIS hybrid method. MCDM (Multi-Criteria Decision Making) programs with MATLAB (R2020b) mathematical package were written to calculate the evaluation results for three distance learning courses. In the practical implementation of the proposed distance learning module evaluation methodology, the experts’ evaluation method was applied. Thirty-four judges were chosen with specific knowledge and skills and with very different competencies to assess three alternatives by fourteen criteria. Following the experts’ evaluation, a statistical analysis method was used to process the data. After applying the complex evaluation, the comparative analysis method was used to summarize the obtained results. This work further provides useful guidelines for the development of an easily understandable hierarchy of criteria model that reflects the main goal of study quality assessment.

scholarly journals INTEGRATED APPROACH OF FUZZY MULTI-ATTRIBUTE DECISION MAKING AND DATA MINING FOR CUSTOMER SEGMENTATION

2021 ◽  
Vol 24 (4) ◽  
pp. 174-188
Author(s):  
Manidatta Ray ◽  
Mamata Ray ◽  
Kamalakanta Muduli ◽  
Audrius Banaitis ◽  
Anil Kumar
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Fuzzy Ahp ◽  
Integrated Approach ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Fuzzy C Means ◽  
Order Preference ◽  
Multi Attribute Decision Making ◽  
Topsis Technique

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy decision environment for customer relationship management. The main objective is to analyse the relation between multi attribute decision making and data mining considering a complex problem of ordering customers segments, which is based on four criteria of customer’s life time value, viz. length (L), recency (R), frequency (F) and monetary value (M). The proposed integrated approach involves fuzzy C-means (FCM) cluster analysis as data mining tool. The experiment conducted using MATLAB 12.0 for identifying eight clusters of customers. The two multi attribute decision making tools i.e., fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are used for ranking these identified clusters. The applicability of the integrated decision making technique is also demonstrated in this paper considering the case of Indian retail sector. This research collected responses from nine experts from Indian retail industry regarding their perception of relative importance of four criteria of customer life value and evaluated weights of each criterion using fuzzy AHP. Transaction data of 18 months of the case retail store was analysed to segment 1,600 customers into eight clusters using fuzzy c-means clustering analysis technique. Finally, these eight clusters were ranked using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The findings of this research could be helpful for firms in identifying the more valuable customers for them and allocate more resources to satisfy them. The findings will be also helpful in developing different loyalty program strategies for customers of different clusters.

scholarly journals APPLICATION OF TOPSIS METHOD WITH TRAPEZOIDAL FUZZY NUMBERS

2021 ◽  
Author(s):  
Salimov Vagif Hasan Oglu
Keyword(s):  
Decision Making ◽  
Test Problem ◽  
Fuzzy Numbers ◽  
Fuzzy Topsis ◽  
Practical Implementation ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Solution Approach ◽  
Application Problem

The article is devoted to the problem of multi-criteria decision making. As application problem is used the equipment selection problem. The analysis of existing methods for solving this problem is given. As a method for solving this problem fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is proposed. This method is based on ideal solution approach. The issues of practical implementation of this method are discussed in details. The results of the solution test problem at all stages are presented.

On Moderate Fuzzy Analytic Hierarchy Process Pairwise Comparison Model With Subcriteria

2016 ◽  
Vol 2 (3) ◽  
pp. 54
Author(s):  
G. Marimuthu ◽  
G. Ramesh
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Decision Models ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analysis Method ◽  
Analytic Hierarchy ◽  
Comparison Model ◽  
Hierarchy Process

Decisions usually involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques.  Every decision making can be considered as the choice from the set of alternatives based on a set of criteria.  The fuzzy analytic hierarchy process is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons mode [10].  The weight vectors from this comparison model are obtained by using extent analysis method.  This paper concern with an alternate method of finding the weight vectors from the original fuzzy AHP decision model (moderate fuzzy AHP model), that has the same rank as obtained in original fuzzy AHP and ideal fuzzy AHP decision models.

scholarly journals Prioritization in Strategic Environmental Assessment Using Fuzzy TOPSIS Method with Random Generation for Absent Information in South Korea

2021 ◽  
Vol 13 (3) ◽  
pp. 1458
Author(s):  
Daeryong Park ◽  
Huan-Jung Fan ◽  
Jun-Jie Zhu ◽  
Taesoon Kim ◽  
Myoung-Jin Um ◽  
...  
Keyword(s):  
Decision Making ◽  
Multicriteria Decision Making ◽  
Fuzzy Topsis ◽  
River Basins ◽  
Ideal Solution ◽  
Missing Information ◽  
Nakdong River ◽  
Han River ◽  
Priority Ranking ◽  
The Nakdong River

This study evaluated a fuzzy technique for order performance by similarity to ideal solution (TOPSIS) as a multicriteria decision making system that compensates for missing information with undefined weight factor criteria. The suggested Fuzzy TOPSIS was applied to ten potential dam sites in three river basins (the Han River, the Geum River, and the Nakdong River basins) in South Korea. To assess potential dam sites, the strategic environment assessment (SEA) monitored four categories: national preservation, endangered species, water quality, and toxic environment. To consider missing information, this study applied the Monte Carlo Simulation method with uniform and normal distributions. The results show that effects of missing information generation with one fuzzy set in GB1 site of the Geum River basin are not great in fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) estimations. However, the combination of two fuzzy sets considering missing information in Gohyun stream (NG) and Hoenggye stream (NH) sites of the Nakdong River basin has a great effect on estimating FPIS, FNIS, and priority ranking in Fuzzy TOPSIS applications. The sites with the highest priority ranking in the Han River, Geum River, and Nakdong River basins based on Fuzzy TOPSIS are the Dal stream 1 (HD1), Bocheong stream 2 (GB2) and NG sites. Among the sites in all river basins, the GB2 site had the highest priority ranking. Consequently, the results coincided with findings of previous studies based on multicriteria decision making with missing information and show the applicability of Fuzzy TOPSIS when evaluating priority rankings in cases with missing information.

Decision Making Model for Electronic Tender Evaluation (eTE) using Fuzzy AHP with Extent Analysis Method

2012 ◽  
Vol 6 (22) ◽  
pp. 79-89 ◽  
Author(s):  
Ros Haslinda Alias ◽  
Noor Maizura Mohamad Noor ◽  
Ali Selamat ◽  
Md Yazid Mohd Saman ◽  
Mohd Lazim Abdullah
Keyword(s):  
Decision Making ◽  
Fuzzy Ahp ◽  
Analysis Method ◽  
Decision Making Model ◽  
Tender Evaluation

Decision making under uncertainty using PEES–fuzzy AHP–fuzzy TOPSIS methodology for landfill location selection

2016 ◽  
Vol 36 (4) ◽  
pp. 351-367 ◽  
Author(s):  
Mohamed Hanine ◽  
Omar Boutkhoum ◽  
Abderrafie El Maknissi ◽  
Abdessadek Tikniouine ◽  
Tarik Agouti
Keyword(s):  
Decision Making ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Decision Making Under Uncertainty ◽  
Location Selection

scholarly journals Refractured Well Selection for Multicriteria Group Decision Making by Integrating Fuzzy AHP with Fuzzy TOPSIS Based on Interval-Typed Fuzzy Numbers

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Tiejun Li ◽  
Jianhua Jin ◽  
Chunquan Li
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Selection Problem ◽  
Triangular Fuzzy Numbers ◽  
Analytic Hierarchy ◽  
Multicriteria Group Decision Making

Multicriteria group decision making (MCGDM) research has rapidly been developed and become a hot topic for solving complex decision problems. Because of incomplete or non-obtainable information, the refractured well-selection problem often exists in complex and vague conditions that the relative importance of the criteria and the impacts of the alternatives on these criteria are difficult to determine precisely. This paper presents a new model for MCGDM by integrating fuzzy analytic hierarchy process (AHP) with fuzzy TOPSIS based on interval-typed fuzzy numbers, to help group decision makers for well-selection during refracturing treatment. The fuzzy AHP is used to analyze the structure of the selection problem and to determine weights of the criteria with triangular fuzzy numbers, and fuzzy TOPSIS with interval-typed triangular fuzzy numbers is proposed to determine final ranking for all the alternatives. Furthermore, the algorithm allows finding the best alternatives. The feasibility of the proposed methodology is also demonstrated by the application of refractured well-selection problem and the method will provide a more effective decision-making tool for MCGDM problems.

Applicability of Seismic Fatigue Evaluation by JSME Code Case, NC-CC-008

2019 ◽  
Author(s):  
Akihito Otani ◽  
Izumi Nakamura ◽  
Tomoyoshi Watakabe ◽  
Masaki Morishita ◽  
Tadahiro Shibutani ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Shaking Table Test ◽  
Shaking Table ◽  
Evaluation Methodology ◽  
Response Analysis ◽  
Analysis Method ◽  
Seismic Evaluation ◽  
Fatigue Evaluation ◽  
Conservative Result ◽  
Current Rule

Abstract A Code Case, JSME S NC1, NC-CC-008, in the framework of JSME Nuclear Codes and Standards has been published. New seismic evaluation methodology for piping by utilizing advanced elastic-plastic response analysis method and strain-based fatigue criteria has been incorporated into the code case. It can achieve more rational seismic design than the current rule. This paper demonstrates validity and applicability of fatigue evaluation method proposed in the code case. Experimental results of a shaking table test for a piping model is used for comparing the evaluation by the current rule with one by the code case. As a result, it is confirmed that the code case can provide a rational and conservative result in the fatigue evaluation of piping. Moreover, cycle counting in the fatigue evaluation was examined for further progress of the code case.

scholarly journals DEA Cross-Efficiency Ranking Method Based on Grey Correlation Degree and Relative Entropy

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 966 ◽  
Author(s):  
Si ◽  
Ma
Keyword(s):  
Relative Entropy ◽  
Evaluation Method ◽  
Ideal Solution ◽  
Analysis Method ◽  
Relational Analysis ◽  
Correlation Degree ◽  
Efficiency Ranking ◽  
Grey Correlation Degree ◽  
Grey Relational ◽  
The Ideal

The ranking of decision-making units (DMUs) is one of the most significant issues in efficiency evaluation. However, the calculation results from the traditional DEA method sometimes include multiple efficient DMUs or multiple DMUs with the same efficiency value, in which case the approach is weak in distinguishing among these DMUs. Therefore, this study proposes a DEA cross-efficiency ranking method based on the relative entropy evaluation method and the grey relational analysis method. First, the approach uses the cross-efficiency matrix as the decision matrix of multiple criteria decision-making (MCDM), and the relationship between DMU and the ideal solution is analyzed by the grey relational analysis method and the relative entropy evaluation method. Then, the degree of the criteria is determined by Shannon entropy, and the weighted grey correlation degree and the weighted relative entropy are obtained. Finally, with the comprehensive relative closeness degree between the DMU and the ideal solution, we can sort all the DMUs accordingly. In a comparative analysis, it shows that this method analyzes the similarity between DMUs and the ideal solution from the information distance and the similarity of the data sequence curve, and has certain advantages for analyzing the ranking of DMUs.

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