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.
Bibliographical noteNOTICE: this is the author’s version of a work that was accepted for publication in Measurement. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Measurement, Vol. 129 (2018) DOI: 10.1016/j.measurement.2018.07.061
- Data envelopment analysis
- Common set of weights
- Multi-period efficiency
- Mean-variance criteria
- Fuzzy logic
Hajiagha, S. H. R., Amoozad Mahdiraji, H., Tavana, M., & Hashemi, S. S. (2018). A novel common set of weights method for multi-period efficiency measurement using mean-variance criteria. Measurement, 129, 569-581. https://doi.org/10.1016/j.measurement.2018.07.061