Title: Cooperative mechanism based on data envelopment analysis and artificial neural network to measure efficiency: case study of Iranian ports
Authors: Raheleh Mousavizadeh; Kaveh Khalili-Damghani
Addresses: Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Tehran, Iran ' Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract: 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 networks; ANNs; efficiency measurement; port efficiency; Iran; decision making units; DMUs; case study; ports.
International Journal of Applied Decision Sciences, 2017 Vol.10 No.1, pp.52 - 68
Available online: 20 Jan 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article