A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

Saeed Yousefi, Roya Soltani, Ali Bonyadi Naeini, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self-organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision-making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.

Original languageEnglish
Article numbere12435
JournalExpert Systems
Issue number5
Publication statusPublished - Oct 1 2019


  • artificial neural networks (ANNs)
  • data envelopment analysis (DEA)
  • double frontier data envelopment analysis
  • power plant
  • robust optimization
  • self-organizing map (SOM)
  • undesirable outputs

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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