Ports are main parts of business and transportation in every country. So, measuring the efficiency of ports is essential. There are several criteria involved in assessment of ports performance. This paper presents a cooperative approach based on data envelopment analysis (DEA) and artificial neural network (ANN) to measure the efficiency and ranking of 11 main ports in Iran. The results show that the DEA-ANN approach outperforms classic DEA approach. The proposed approach provides following advantages: 1) high discrimination power in presence of low number of decision making units (DMUs) and high number of inputs and outputs; 2) suitable estimation of future production function; 3) providing a proper estimation of future efficiency scores; 4) no need to use super-efficiency models to rank the DMUs. The proposed model of this study can help managers to evaluate the performance of DMUs on the basis of future estimated inputs and outputs.Keywords: data envelopment analysis; DEA; artificial neural network; ANN; hybrid DEA-ANN; efficiency measurement; port efficiency; Iran. He has published more than 80 scientific papers in international journals and conferences. He serves as an Associate Editor of six international scientific journals. His researches can be found at: http://kaveh-khalili.webs.com. His research interests are computational intelligence, soft computing, data envelopment analysis, fuzzy optimisation and decision-making, applied operations research.
Ports are main parts of business and transportation in every country. So, measuring the efficiency of ports is essential. There are several criteria involved in assessment of ports performance. This paper presents a cooperative approach based on data envelopment analysis (DEA) and artificial neural network (ANN) to measure the efficiency and ranking of 11 main ports in Iran. The results show that the DEA-ANN approach outperforms classic DEA approach. The proposed approach provides following advantages: 1) high discrimination power in presence of low number of decision making units (DMUs) and high number of inputs and outputs; 2) suitable estimation of future production function; 3) providing a proper estimation of future efficiency scores; 4) no need to use super-efficiency models to rank the DMUs. The proposed model of this study can help managers to evaluate the performance of DMUs on the basis of future estimated inputs and outputs.Keywords: data envelopment analysis; DEA; artificial neural network; ANN; hybrid DEA-ANN; efficiency measurement; port efficiency; Iran. He has published more than 80 scientific papers in international journals and conferences. He serves as an Associate Editor of six international scientific journals. His researches can be found at: http://kaveh-khalili.webs.com. His research interests are computational intelligence, soft computing, data envelopment analysis, fuzzy optimisation and decision-making, applied operations research.
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