Modeling the future efficiency of the green supply chain in the poultry farming industry using multi-stage DEA and artificial neural networks
Tahere Torkashvand
1
(
)
Fatemeh saghafi
2
(
Associate Prof. of University of Tehran
)
MOHAMMAD HOSSEIN DARVISH MOTEVALLI
3
(
Department of Industrial Management, West Tehran Branch, Islamic Azad University, Tehran, Iran.
)
Keywords: Artificial neural network, multi-stage data envelopment analysis, green supply chain of poultry farming industry,
Abstract :
Traditional DEA and multi-stage DEA models cannot predict future performance. In other words, all DEA and multi-stage DEA models evaluate units based on past performance. This paper seeks to move from previous monitoring models to a future planning approach that includes new contributions. In this study, artificial neural networks (ANN) are combined with multi-stage DEA to predict the future performance of the green supply chain of the poultry industry. For this purpose, first, after identifying the indicators affecting the green supply chain by combining the fuzzy Delphi method and the BWM method, 8 input indicators and 8 output indicators were identified, and after collecting the data of the studied chains using the artificial neural network, the data was predicted for 5 years. Then, the predicted data from the artificial neural network was used in multi-stage DEA. Multi-stage DEA evaluates the performance of the green poultry supply chain in the past, present, and future periods simultaneously. These results are useful for policy making and efficiency performance trend analysis.
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