Remarks on the analytic hierarchy process
Management Science
Generating consensus priority point vectors: a logarithmic goal programming approach
Computers and Operations Research
Group prioritization in the AHP by fuzzy preference programming method
Computers and Operations Research
Linear programming models for estimating weights in the analytic hierarchy process
Computers and Operations Research
Combining different prioritization methods in the analytic hierarchy process synthesis
Computers and Operations Research
Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process
Computers and Operations Research
An approach to avoiding rank reversal in AHP
Decision Support Systems
Information Sciences: an International Journal
Comparison judgments in incomplete Saaty matrices
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
A study of developing an input-oriented ratio-based comparative efficiency model
Expert Systems with Applications: An International Journal
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Enumerating all spanning trees for pairwise comparisons
Computers and Operations Research
Some models for generating and ranking multiplicative weights
Computers and Industrial Engineering
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This paper proposes a linear programming method for generating the most favorable weights (LP-GFW) from pairwise comparison matrices, which incorporates the variable weight concept of data envelopment analysis (DEA) into the priority scheme of the analytic hierarchy process (AHP) to generate the most favorable weights for the underlying criteria and alternatives on the basis of a crisp pairwise comparison matrix. The proposed LP-GFW method can generate precise weights for perfectly consistent pairwise comparison matrices and approximate weights for inconsistent pairwise comparison matrices, which are not too far from Saaty's principal right eigenvector weights. The issue of aggregation of local most favorable weights and rank preservation methods is also discussed. Four numerical examples are examined using the LP-GFW method to illustrate its potential applications and significant advantages over some existing priority methods.