Using the Nash Bargaining Method for Performance Evaluation and Target Setting with Grey Data
محورهای موضوعی : فصلنامه ریاضیArezoo Taheri Kamran 1 , Mohsen Rostami 2 , Farad Hosenzadeh 3
1 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Data Envelopment Analysis, Grey Data, Nash Bargaining, Grey Data Envelopment Analysis.,
چکیده مقاله :
There are numerous Data Envelopment Analysis (DEA) applications where the data is not accurate. In many real-world scenarios, data is inaccurate. One type of inaccurate data is grey data, which exists between the fully defined boundaries of structured and unstructured data. This article employs the Nash Bargaining approach for evaluation and target setting. We combine grey data DEA scores with the Nash Bargaining problem to find an equilibrium point between the minimum and maximum efficiency values for each DMU. Based on the bargaining method, the equilibrium point is determined for each of the DMUs as a weighted average or relative equilibrium point between the minimum and maximum efficiency. The proposed approach has been validated on different datasets according to grey data.
There are numerous Data Envelopment Analysis (DEA) applications where the data is not accurate. In many real-world scenarios, data is inaccurate. One type of inaccurate data is grey data, which exists between the fully defined boundaries of structured and unstructured data. This article employs the Nash Bargaining approach for evaluation and target setting. We combine grey data DEA scores with the Nash Bargaining problem to find an equilibrium point between the minimum and maximum efficiency values for each DMU. Based on the bargaining method, the equilibrium point is determined for each of the DMUs as a weighted average or relative equilibrium point between the minimum and maximum efficiency. The proposed approach has been validated on different datasets according to grey data.
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