A novel common set of weights method for multi-period efficiency measurement using mean-variance criteria

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Data envelopment analysis (DEA) is a popular method for evaluating a set of homogeneous decision-making units (DMUs). One of the main shortcomings of DEA is the weights flexibility where each unit can take its desirable weights. Several methods have been developed for finding a common set of weights (CSWs) and overcoming this drawback. The CSWs methods are used to evaluate the relative efficiency of the DMUs in a single time-period. However, single period DEA models cannot handle organizational units performing in a continuum of time. We propose a novel method for determining the CSWs in a multi-period DEA. Initially, the CSWs problem is formulated as a multi-objective fractional programming problem. Subsequently, a multi-period form of the problem is formulated and the mean efficiency of the DMUs is maximized while their efficiency variances is minimized. A fuzzy set-based approach is used to solve the multi-period CSWs problem. We present a real-world case study to demonstrate applicability and exhibit the efficacy of the proposed method. The results indicate a significant improvement in the discrimination power of the proposed multi-period method.




Razavi Hajiagha, S.H., Amoozad Mahdiraji, H., Tavana, M. and Sadat Hashemi, S. (2018) ‘A Novel Common Set of Weights Method for Multi-Period Efficiency Measurement Using Mean-Variance Criteria,’ Measurement, Vol. 129, pp. 569-581.