Approaches to consistency adjustment
Journal of Optimization Theory and Applications
The theory of ratio scale estimation: Saaty's analytic hierarchy process
Management Science
Linear programming models for estimating weights in the analytic hierarchy process
Computers and Operations Research
Consensus-based intelligent group decision-making model for the selection of advanced technology
Decision Support Systems
Consensus models for AHP group decision making under row geometric mean prioritization method
Decision Support Systems
A web based consensus support system for group decision making problems and incomplete preferences
Information Sciences: an International Journal
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
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An assessment of the individual judgments and AHP-produced priority vectors for involved decision-makers indicates that the individual consistencies of decision makers may vary significantly, thus making the final group decision less reliable. In this paper, an approach is proposed as to how to combine decision makers' local priority vectors in AHP synthesis and reduce so-called group inconsistency. Instead of aggregating individual judgments (AIJ), or aggregating individually derived final priorities (AIP), we propose to perform an AHP synthesis of the best local priority vectors taken from the most consistent decision makers. The approach and related algorithm we label as MGPS after the key terms 'multicriteria group prioritization synthesis.' The concept is analogous to the one proposed by Srdjevic [1] for individual AHP applications where the best local priority vectors are selected based on the consistency performance of several of the most popular prioritization methods. Here, decision makers are combined instead of prioritization methods, and group context is fully implemented. After completing an evaluation of the decision makers inconsistencies in each node of the hierarchy, the selected best local priority vectors are synthesized in a standard manner, and the final solution is declared to be an AHP-group decision. Two numerical examples indicate that the developed approach and algorithm generate the final priorities of alternatives with the lowest overall inconsistency (in the multicriteria sense).