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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
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An extended data envelopment analysis for the decision-making.

Xiao-Li Meng1,2, Fu-Gui Shi1,2

  • 1School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, 100081 China.

Journal of Inequalities and Applications
|October 20, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an extended data envelopment analysis (DEA) for evaluating decision-making unit efficiency using historical data. The novel approach incorporates time-series analysis and a binary search algorithm for improved accuracy and computational efficiency.

Keywords:
binary search treedata envelopment analysisdecision-makingsample standardstime series analysis

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Area of Science:

  • Operations Research
  • Management Science
  • Econometrics

Background:

  • Traditional Data Envelopment Analysis (DEA) models often assume static inputs and outputs.
  • Evaluating decision-making units (DMUs) with time-varying data presents challenges for standard DEA.
  • Existing methods may struggle with large datasets and the need for comparative efficiency analysis.

Purpose of the Study:

  • To develop an extended DEA model for assessing DMU efficiency using historical, time-series data.
  • To enhance efficiency evaluation by incorporating ordered sample standards and inter-DMU efficiency relationships.
  • To improve computational efficiency through a novel binary search tree-based algorithm for sample standard selection.

Main Methods:

  • Extension of the CCR (Charnes, Cooper, Rhodes) DEA model.
  • Application of time-series analysis for input and output data prediction.
  • Development of a binary search tree algorithm for efficient sample standard selection.
  • Comparative efficiency analysis considering relationships between DMUs.

Main Results:

  • The proposed extended DEA model effectively evaluates DMU efficiency with variable historical data.
  • The time-series component allows for dynamic data analysis and prediction.
  • The binary search algorithm significantly reduces computational complexity in selecting appropriate sample standards.
  • Numerical examples demonstrate the model's applicability and effectiveness.

Conclusions:

  • The extended DEA model provides a robust framework for efficiency evaluation with dynamic data.
  • The integration of time-series analysis and advanced algorithms enhances analytical capabilities.
  • This approach offers a more accurate and computationally efficient method for assessing DMU performance in complex scenarios.