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 Research on Knowledge Management Models at Universities Using Fuzzy Analytic Hierarchy Process (FAHP)

2021 ◽  
Vol 13 (2) ◽  
pp. 809
Author(s):  
Ngoc Thach Pham ◽  
Anh Duc Do ◽  
Quang Vinh Nguyen ◽  
Van Loi Ta ◽  
Thi Thanh Binh Dao ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Analytic Hierarchy Process ◽  
Fuzzy Analytic Hierarchy Process ◽  
Data Systems ◽  
Knowledge Use ◽  
Analytic Hierarchy ◽  
Priority Order ◽  
Big Data Systems ◽  
Document Review ◽  
Hierarchy Process

This study aims to investigate and evaluate factors related to the knowledge management model at universities in Hanoi, Vietnam. Based on the system literature review (SLR) approach, the study follows descriptive and inductive approach results of the document review process. Eight factors were synthesized with the fuzzy analytic hierarchy process (FAHP) to evaluate the priority order. Ten experts from seven universities participated in the survey. The results rank as follows: (1) knowledge sharing factor (this also has the highest best nonfuzzy performance (BNP) and average multiplier weight (GM)); (2) knowledge management with big data systems; (3) knowledge creation; (4) knowledge use; (5) knowledge gathering; (6) leadership; (7) knowledge rating; and (8) knowledge storage. Discussions, conclusions, limitations of the study, and suggestions for future studies are also mentioned in this study.

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