Adding and removing an attribute in a DEA model: theory and processing

We present a theoretical and computational study of the impact of inserting a new attribute and removing an old attribute in a data envelopment analysis (DEA) model. Our objective is to obviate a portion of the computational effort needed to process such model changes by studying how the efficient/i...

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Bibliographic Details
Published in:The Journal of the Operational Research Society 2008-12, Vol.59 (12), p.1674-1684
Main Authors: López, F. J., Dula, J. H., Dutá, J. H.
Format: Article
Language:English
Subjects:
Applied sciences
Business and Management
Classification
computational analysis
computational geometry
Computer analysis
convex analysis
Coordinate systems
Data envelopment analysis
data envelopment analysis (DEA)
Datasets
Decision making units
Decision theory. Utility theory
Design efficiency
Efficiency
Exact sciences and technology
Firm modelling
Hyperplanes
Inner products
Linear programming
Management
Mathematical programming
Mathematical vectors
Mathematics
Nonparametric inference
Operational research and scientific management
Operational research. Management science
Operations research
Operations Research/Decision Theory
polyhedral set theory
Probability and statistics
Production efficiency
Production possibilities
Sciences and techniques of general use
Statistics
Studies
Theoretical Paper
Theoretical Papers
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Summary:We present a theoretical and computational study of the impact of inserting a new attribute and removing an old attribute in a data envelopment analysis (DEA) model. Our objective is to obviate a portion of the computational effort needed to process such model changes by studying how the efficient/inefficient status of decision-making units (DMUs) is affected. Reducing computational efforts is important since DEA is known to be computationally intensive, especially in large-scale applications. We present a comprehensive theoretical study of the impact of attribute insertion and removal in DEA models, which includes sufficient conditions for identifying efficient DMUs when an attribute is added and inefficient DMUs when an attribute is removed. We also introduce a new procedure, HyperClimb, specially designed to quickly identify some of the new efficient DMUs, without involving LPs, when the model changes with the addition of an attribute. We report on results from computational tests designed to assess this procedure's effectiveness.
ISSN:0160-5682
1476-9360
DOI:10.1057/palgrave.jors.2602505