Premium
A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty
Author(s) -
Yousefi Saeed,
Soltani Roya,
Bonyadi Naeini Ali,
Farzipoor Saen Reza
Publication year - 2019
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12435
Subject(s) - data envelopment analysis , computer science , frontier , artificial neural network , envelopment , efficient frontier , power (physics) , rank (graph theory) , sustainability , mathematical optimization , data mining , artificial intelligence , mathematics , economics , portfolio , ecology , archaeology , combinatorics , biology , financial economics , history , physics , quantum mechanics
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.