Additive DEA based on MCDA with imprecise information

• Published in: The Journal of the Operational Research Society Vol. 59; no. 1; pp. 54 - 63
• Main Authors: Gouveia, M C, Dias, L C, Antunes, C H
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.01.2008; Palgrave Macmillan Press; Palgrave Macmillan UK; Palgrave; Taylor & Francis Ltd
• Subjects: Applied sciences; Business and Management; Data Envelopment Analysis; Decision analysis; Decision making units; Decision theory. Utility theory; Efficiency; Efficiency metrics; Efficiency objectives; Exact sciences and technology; Extra dimensions; Firm modelling; Hyperplanes; imprecise information; Linear programming; Management; Mathematical models; Mathematics; Modeling; multi-criteria analysis; Multiple criteria decision making; Nonparametric inference; Objective functions; Operational research and scientific management; Operational research. Management science; Operations research; Operations Research/Decision Theory; Probability and statistics; Sciences and techniques of general use; Statistics; Studies; Term weighting; Theoretical Paper; Theoretical Papers; Utility functions; multi-criteria analysis; imprecise information; decision analysis; Data Envelopment Analysis; Performance evaluation; Input output; Role playing; Multicriteria analysis; Decision making; Utility function; Non parametric estimation; Decision analysis; Modeling; Multicomponent analysis; Utility theory; Weight function; Imperfect information; Incomplete information; Data envelopment analysis
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/palgrave.jors.2602317

This work exploits links between Data Envelopment Analysis (DEA) and multicriteria decision analysis (MCDA), with decision making units (DMUs) playing the role of decision alternatives. A novel perspective is suggested on the use of the additive DEA model in order to overcome some of its shortcomings, using concepts from multiattribute utility models with imprecise information. The underlying idea is to convert input and output factors into utility functions that are aggregated using a weighted sum (additive model of multiattribute utility theory), and then let each DMU choose the weights associated with these functions that minimize the difference of utility to the best DMU. The resulting additive DEA model with oriented projections has a clear rationale for its efficiency measures, and allows meaningful introduction of constraints on factor weights.