DATA ENVELOPMENT ANALYSIS and Confident-DEA

This work contributes to the Data
Envelopment Analysis (DEA) literature at three ways.
First, it extends the roots of DEA by providing an
analytical approach deriving the basic Charnes-
Cooper-Rhodes (1978) model from the Weak Axiom of
Profit Maximization (WAPM) of Firm Theory.
Second, this work provides a systematic way for
classifying the existing DEA literature by offering
a taxonomy. Finally, a theoretical contribution to
the literature, Confident-DEA approach, is proposed
involving a bilevel convex optimization model to
which a Genetic-Algorithm-based solution method is
suggested. Complementing previous DEA methodologies,
which provides single valued efficiency measures,
Confident-DEA provides a range of values for the
efficiency measures, an efficiency confidence
interval and hence the name, reflecting the
imprecision in data. Monte-Carlo simulation is used
to determine the distribution of the efficiency
measures, taking into account the distribution of
the bounded imprecise data over their corresponding
intervals. Confident-DEA is applied to predict the
efficiency of banking systems in OECD countries.

Autorentext

Dr Said Gattoufi Received his Ph.D. in Management, option OR, from Sabanci University in Turkey in 2002. His research areas cover DEA in Banking, Evolutionary algorithms for Imprecise DEA, M&A in Banking, Meta-research and e-learning. He is currently the Head of Operations Management Department at Sultan Qaboos University in Oman.

Klappentext

This work contributes to the Data Envelopment Analysis (DEA) literature at three ways. First, it extends the roots of DEA by providing an analytical approach deriving the basic Charnes-Cooper-Rhodes (1978) model from the Weak Axiom of Profit Maximization (WAPM) of Firm Theory. Second, this work provides a systematic way for classifying the existing DEA literature by offering a taxonomy. Finally, a theoretical contribution to the literature, Confident-DEA approach, is proposed involving a bilevel convex optimization model to which a Genetic-Algorithm-based solution method is suggested. Complementing previous DEA methodologies, which provides single valued efficiency measures, Confident-DEA provides a range of values for the efficiency measures, an efficiency confidence interval and hence the name, reflecting the imprecision in data. Monte-Carlo simulation is used to determine the distribution of the efficiency measures, taking into account the distribution of the bounded imprecise data over their corresponding intervals. Confident-DEA is applied to predict the efficiency of banking systems in OECD countries.