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A DEA-Benchmarking Optimization Model and Method Based on the Theory of Maximum Entropy

  • Yin-sheng Yang
  • Ning Li
  • Hai-cun Liu
  • Hong-peng Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

Benchmarking is a technique that engages and executes a series of measures to change indexes of the Decision Making Unit (DMU) to excellent by using the gap analysis information between the DMU and benchmark. In this paper, a DEA-Benchmarking model based on the theory of maximum entropy is proposed and the conception of Entropy-DEA efficiency is defined. According to the optimization model based on the theory of maximum entropy, the Entropy-DEA efficient DMUs is regarded as benchmarks, which have more advantages and direction than DEA efficient DMUs. The measure method and existence property of Entropy-DEA efficiency are all analyzed in this paper.

Keywords

Data Envelopment Analysis Maximum Entropy Information Entropy Decision Make Unit Efficient DMUs 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yin-sheng Yang
    • 1
  • Ning Li
    • 1
  • Hai-cun Liu
    • 1
  • Hong-peng Guo
    • 1
  1. 1.School of Biological and Agricultural EngineeringJilin UniversityChangchunP.R. China

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