A technique for identifying congestion in Data Envelopment Analysis
محورهای موضوعی : International Journal of Data Envelopment Analysis
Amin Jabbari
1
(Department of Mathematics, Science and Research branch, Islamic Azad University, Tehran, Iran.)
Farhad Hosseinzadeh Lotfi
2
(Department of Mathematics, Science and Research branch, Islamic Azad University, Tehran, Iran.)
Mohsen Rostamy Malkhalifeh
3
(Department of Mathematics, Science and Research Branch, Islamic Azad University,Tehran, Iran)
کلید واژه: DEA, Congestion, Efficiency, Inefficiency,
چکیده مقاله :
Data Envelopment Analysis (DEA) is a non-parametric mathematical programming method used to assess performance and measure the efficiency of Decision-making Units (DMUs) that operate with multiple concurrent inputs and outputs. The performance of these units is influenced by the utilization of input resources. While an increase in input utilization typically leads to higher production levels, there are scenarios where increased input usage results in decreased outputs. This phenomenon is termed congestion. Given that alleviating congestion can reduce costs and enhance production, it holds significant importance in economics. This paper introduces a method for identifying congestion based on a defined modeling framework. A DMU is considered congested when reducing inputs in at least one component leads to increased outputs in at least one component, and increasing inputs in at least one component can be achieved by reducing outputs in at least one component, without improvement in other indicators. The paper explores congestion in DMUs with both increasing and decreasing inputs.
Data Envelopment Analysis (DEA) is a non-parametric mathematical programming method used to assess performance and measure the efficiency of Decision-making Units (DMUs) that operate with multiple concurrent inputs and outputs. The performance of these units is influenced by the utilization of input resources. While an increase in input utilization typically leads to higher production levels, there are scenarios where increased input usage results in decreased outputs. This phenomenon is termed congestion. Given that alleviating congestion can reduce costs and enhance production, it holds significant importance in economics. This paper introduces a method for identifying congestion based on a defined modeling framework. A DMU is considered congested when reducing inputs in at least one component leads to increased outputs in at least one component, and increasing inputs in at least one component can be achieved by reducing outputs in at least one component, without improvement in other indicators. The paper explores congestion in DMUs with both increasing and decreasing inputs.
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