A Malmquist Productivity Index with Directional Distance Function and Uncertain Data

Abstract

In the present study, by using the directional distance function with undesirable interval outputs, the Malmquist productivity index (MPI) and integrated data envelopment analysis (DEA) are presented for evaluating the function of decision making unites (DMUs). The MPI calculation is performed to compare the efficiency of the DMUs in distinct time periods. The uncertainty inherent in real-world problems is considered by using the best and worst-case scenarios, defining an interval for the MPI and reflecting the DMUs' advancement or regress. The optimal solution of the robust model lies in the efficiency interval, i.e., it is always equal to or less than the optimal solution in the optimistic case and equal to or greater than the optimal solution in the pessimistic case. We also present a case study in the banking industry to demonstrate the applicability and efficacy of the proposed integrated approach.

The Construction and Empirical Research on the Dynamic Evaluation Model of University Science and Technology Output

“…In this study, we shunned the traditional radial measurement DEA and BCC models in favor of an improved Enhanced Russell Measure. Concretely, the integrated data envelopment analysis (DEA) and Malmquist productivity index (MPI) used to evaluate the performance of decision making units (DMUs) can go beyond static performance to detect the temporal variations resulting from efforts for betterment by using historic data panels [41]. We used this integrated evaluation methodology to establish an effective dynamic evaluation model for the scientific and technological output of universities and the intention of the study was to propose a national-scale comparative measurement of university technical and allocative efficiency.…”

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

“…They put forward an integrated multi-response model for CO 2 laser cutting by suggesting benchmarks for all DMUs, regardless of being efficient or not. Aghayi et al (2019) proposed a DEA approach using the Malmquist Index (MI) to rank the inputs and outputs based on their relative distance from the efficiency frontier. The MI helps the decision-maker evaluate and analyze inefficiencies or efficiencies accurately.…”

Personnel Expenses and Productivity Change in Peruvian Stock Mining Companies

“…Farrel (1957) conceived DEA as an efficiency analysis tool; however, further research has made it possible to employ DEA as a productivity change tool by the Malmquist Index. This index was developed by Caves, Christiensen and Diewert in 1982, who improved former Malmquistś ratios distance function research published in 1953 (Aghayi et al, 2019). Also, they took concepts of Shephardś theoretical indexes distance functions (Mohammadi & Ranaei, 2011).…”