Axiomatic foundation of the analytic hierarchy process
Management Science
Remarks on the analytic hierarchy process
Management Science
Some thoughts on research in multiple criteria decision making
Computers and Operations Research - Special issue: implementing multiobjective optimization methods: behavioral and computational issues
Decision quality using ranked attribute weights
Management Science
Generating consensus priority point vectors: a logarithmic goal programming approach
Computers and Operations Research
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: A Comprehensive Text with Models, Applications References, and DEA-Solver Software with Cdrom
Data Envelopment Analysis: The Assessment of Performance
Data Envelopment Analysis: The Assessment of Performance
The Analytic Hierarchy Process--An Exposition
Operations Research
An Introduction to Data Envelopment Analysis
An Introduction to Data Envelopment Analysis
A common framework for deriving preference values from pairwise comparison matrices
Computers and Operations Research
Railway station site selection using analytical hierarchy process and data envelopment analysis
Computers and Industrial Engineering
Enumerating all spanning trees for pairwise comparisons
Computers and Operations Research
A linear programming approximation to the eigenvector method in the analytic hierarchy process
Information Sciences: an International Journal
Sustainable R&D portfolio assessment
Decision Support Systems
Benchmarking of service quality with data envelopment analysis
Expert Systems with Applications: An International Journal
Interactive minimax optimisation for integrated performance analysis and resource planning
Computers and Operations Research
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Data envelopment analysis (DEA) is proposed in this paper to generate local weights of alternatives from pair-wise comparison judgment matrices used in the analytic hierarchy process (AHP). The underlying assumption behind the approach is explained, and some salient features are explored. It is proved that DEA correctly estimates the true weights when applied to a consistent matrix formed using a known set of weights. DEA is further proposed to aggregate the local weights of alternatives in terms of different criteria to compute final weights. It is proved further that the proposed approach, called DEAHP in this paper, does not suffer from rank reversal when an irrelevant alternative(s) is added or removed.