Completion of the paper “Ranking Efficient DMUs based on single virtual inefficient DMU in DEA”
محورهای موضوعی : Mathematical Optimization
AmirReza Hassani Tabatabaee
1
(Department of Industrial Management, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran. )
امیر غلام ابری
2
(دانشگاه آزاد اسلامی)
Shokrollah Ziari
3
(Department of Mathematics, South Tehran branch, Islamic Azad University, Tehran, Iran. )
ebrahim niknaghsh
4
(Department of management, FiroozKooh Branch, Islamic Azad University, FiroozKooh, Iran)
کلید واژه: Data Envelopment Analysis (DEA), Decision Making Units (DMU), Ranking, Virtual DMU, Anti Ideal Point (AIP),
چکیده مقاله :
This paper builds upon the foundation laid by Shetty's research, aiming to enhance our understanding of decision-making unit (DMU) efficiency. In doing so, we introduce a novel approach that offers a more comprehensive method for ranking DMUs. By aggregating this information, we establish a benchmark against which the efficiency of individual DMUs can be assessed. This approach not only simplifies the evaluation process but also provides a more holistic perspective, enabling researchers to discern patterns and trends across the entire dataset. The method proposed in the aforementioned paper proved to be efficacious particularly in scenarios where the number of efficient DMUs was limited, enabling the model to accurately rank them. Therefore, as the number of efficient DMUs escalates, the effectiveness of the proposed methodology in accurately ranking them may diminish. Efficient Decision Making Units (DMUs) construct the defining hyperplane; therefore, the exclusion of these contributing efficient DMUs in an attempt to derive their ranking, amidst an increase in their numbers, will impede the acquisition of efficiency scores for virtual DMUs. Hence, achieving a comprehensive ranking of all DMUs is unattainable unless those positioned precisely on the defining hyperplane are included. In this complementary method, we delineate an anti-ideal virtual DMU encompassing all DMUs situated on the corresponding defining hyperplane, which may be oriented in various directions. Then, we use this method for ranking efficient DMUs. As the proposed method aligns with the aforementioned study, it incorporates all the advantages, including simplicity and stability, and notably eliminates the identified flaw.
This paper builds upon the foundation laid by Shetty's research, aiming to enhance our understanding of decision-making unit (DMU) efficiency. In doing so, we introduce a novel approach that offers a more comprehensive method for ranking DMUs. By aggregating this information, we establish a benchmark against which the efficiency of individual DMUs can be assessed. This approach not only simplifies the evaluation process but also provides a more holistic perspective, enabling researchers to discern patterns and trends across the entire dataset. The method proposed in the aforementioned paper proved to be efficacious particularly in scenarios where the number of efficient DMUs was limited, enabling the model to accurately rank them. Therefore, as the number of efficient DMUs escalates, the effectiveness of the proposed methodology in accurately ranking them may diminish. Efficient Decision Making Units (DMUs) construct the defining hyperplane; therefore, the exclusion of these contributing efficient DMUs in an attempt to derive their ranking, amidst an increase in their numbers, will impede the acquisition of efficiency scores for virtual DMUs. Hence, achieving a comprehensive ranking of all DMUs is unattainable unless those positioned precisely on the defining hyperplane are included. In this complementary method, we delineate an anti-ideal virtual DMU encompassing all DMUs situated on the corresponding defining hyperplane, which may be oriented in various directions. Then, we use this method for ranking efficient DMUs. As the proposed method aligns with the aforementioned study, it incorporates all the advantages, including simplicity and stability, and notably eliminates the identified flaw.
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