Common weights for fully ranking decision making units by regression analysis

Authors:
Ying-Ming Wang;Ying Luo;Yi-Xin Lan
Affiliations:
School of Public Administration, Fuzhou University, Fuzhou 350108, PR China;School of Management, Xiamen University, Xiamen 361005, PR China;School of Public Administration, Fuzhou University, Fuzhou 350108, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 4
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Abstract

Existing methods for generating common weights in data envelopment analysis (DEA) are either very complicated or unable to produce a full ranking for decision making units (DMUs). This paper proposes a new methodology based on regression analysis to seek a common set of weights that are easy to estimate and can produce a full ranking for DMUs. The DEA efficiencies obtained with the most favorable weights to each DMU are treated as the target efficiencies of DMUs and are best fitted with the efficiencies determined by common weights. Two new nonlinear regression models are constructed to optimally estimate the common weights. Four numerical examples are examined using the developed new models to test their discrimination power and illustrate their potential applications in fully ranking DMUs. Comparisons with a similar compromise approach for generating common weights are also discussed.