Mohsen Rostamy‐Malkhalifeh
Biographic Data
| ID | 6829464 |
|---|---|
| NAME | Mohsen Rostamy‐Malkhalifeh |
| GIVEN NAMES | Mohsen |
| FAMILY NAME | Rostamy‐Malkhalifeh |
| SIGNATURE | MALKHALIFEH M R |
| AFFILIATIONS | Islamic Azad University, Science and Research Branch |
| ORCID | 0000-0001-6105-7674 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
An investigation of the undesirable outputs congestion in data envelopment analysis
Congestion is one of the most important subjects in Data Envelopment Analysis (DEA) which helps the Decision Maker (DM) to decide about changing the size of units. The estimation of congestion has attractive advantages from different perspectives. For example, the total cost of a partiular DMU, in which the congestion occurs, can be reduced by the decreases in inputs. On the other hand, the output of units can be increased by the recognizing and …
Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis
Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forec…
Ranking of units by corrected cross-efficiency method using optimal weights in the smallest interval
An important method for ranking of decision making units (DMUs) in data envelopment analysis (DEA) is cross-efficiency method. This study proposes a secondary multi-objective model for calculating optimal weights with least dispersion. Firstly, these weights are placed in the smallest interval. Secondly, the cross-efficiency of each of the other units has the least deviation from the CCR efficiency of the same unit. Therefore, optimal weights are…
No prominent works on this page.
An investigation of the undesirable outputs congestion in data envelopment analysis
Congestion is one of the most important subjects in Data Envelopment Analysis (DEA) which helps the Decision Maker (DM) to decide about changing the size of units. The estimation of congestion has attractive advantages from different perspectives. For example, the total cost of a partiular DMU, in which the congestion occurs, can be reduced by the decreases in inputs. On the other hand, the output of units can be increased by the recognizing and …
Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis
Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forec…
Ranking of units by corrected cross-efficiency method using optimal weights in the smallest interval
An important method for ranking of decision making units (DMUs) in data envelopment analysis (DEA) is cross-efficiency method. This study proposes a secondary multi-objective model for calculating optimal weights with least dispersion. Firstly, these weights are placed in the smallest interval. Secondly, the cross-efficiency of each of the other units has the least deviation from the CCR efficiency of the same unit. Therefore, optimal weights are…
Computer Science (3 works) · Data envelopment analysis (3 works) · Efficiency Analysis Using DEA (3 works) · Mathematical optimization (3 works) · Mathematics (3 works) · Interval (graph theory (2 works) · Statistics (2 works) · Artificial Intelligence (1 work) · Combinatorics (1 work) · Confidence interval (1 work)