Eliminating congestion of decision-making units using inverse data envelopment analysis

Document Type : Research Paper

Authors

1 Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Mathematics, Khomeinishar branch, Islamic Azad University, Isfahan, Iran.

Abstract

This survey proposes a new application of the inverse data envelopment analysis (InvDEA) in the problem of merging decision-making units (DMUs) to improve the performance of DMUs by removing congestion. Congestion is a factor in reducing production; therefore, removing it decreases costs and increases outputs. There are two significant subjects in the merging DMUs. Estimating the inherited inputs and outputs of a new production DMU with no congestion is the first problem while achieving a pre-specified efficiency level from the merged DMU is the second one. Both problems are examined using the ideas of inverse DEA and congestion. Using Pareto solutions to multiple-objective programming problems, sufficient conditions for inherited input/output estimates with no congestion and increasing efficiency are created. Besides, an example is perused for the reliability of the proposed approach in basic research institutes in the Chinese Academy of Science (CAS) in 2010.

Keywords

Main Subjects

References

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History

  • Receive Date: 10 September 2023
  • Revise Date: 25 April 2024
  • Accept Date: 14 June 2024

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