1994
DOI: 10.1016/0377-2217(94)90356-5
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Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members' weightages

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Cited by 489 publications
(232 citation statements)
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“…Islam [11] developed certain techniques to extract the underlying weights from different types of pairwise comparison matrices in the framework of analytic hierarchy process. Ramanathan and Ganesh [19] proposed a simple and appealing eigenvector-based method to intrinsically determine the weightages for group members using their own subjective opinions and also its superiority over other methods. Prabhu and VizayaKumar [18] illustrated the use of fuzzy hierarchical decisionmaking (FHDM) for steel-making technology considering the Indian conditions.…”
Section: Introductionmentioning
confidence: 99%
“…Islam [11] developed certain techniques to extract the underlying weights from different types of pairwise comparison matrices in the framework of analytic hierarchy process. Ramanathan and Ganesh [19] proposed a simple and appealing eigenvector-based method to intrinsically determine the weightages for group members using their own subjective opinions and also its superiority over other methods. Prabhu and VizayaKumar [18] illustrated the use of fuzzy hierarchical decisionmaking (FHDM) for steel-making technology considering the Indian conditions.…”
Section: Introductionmentioning
confidence: 99%
“…The weights that are assigned to the arithmetic mean represent the relative significance of the SMEs and/or methods. The SMEs could either assign a weight to each other (Ramanathan and Ganesh 1994), or SMEs with lower matrix inconsistency ratios could be assigned higher weights (Cho and Cho 2008), or one SME could act as a supra decision maker and assign weights to others based on their merits. However, as these conditions were not applicable for this research, equal weights were assigned to all SMEs and methods.…”
Section: Deriving the Final Ranking Setsmentioning
confidence: 99%
“…an advantage in presenting a common opinion by simply aggregating the opinions of many evaluators. 48 In addition, the model is easy to understand, can easily obtain information from decision-makers, and deals with both qualitative and quantitative data.…”
Section: Process Miningmentioning
confidence: 99%