Abstract
This work intends to integrate artificial neural network (ANN) and data envelopment analysis (DEA) in a single framework to evaluate the performance of operations in the port container terminal. The proposed framework is based on three steps. In the first step, we identify the performance measures objectives and the indicators affecting our system. In the second step, a DEA-based oriented inputs model (DEA-CCR) is used to compute the efficiency scores of the system, based on the obtained scores, the data is divided into training and testing datasets. In the last step, an Improved Sine-Cosine Algorithm (ISCA) is employed as a new method for training FNNs to determine the efficiency scores. In ISCA, the so-called Levy flights is used to enhance the convergence rate of SCA and prevent it from getting stuck in local optima. To demonstrate the efficacy of the proposed framework, it is utilized to evaluate the performance of two ports container terminal mainly: Tangier and Casablanca. The results are compared with a standard BBO, GA and PSO-based learning algorithm. The new trainer ISCA is also investigated and evaluated using four different classification datasets selected from the UCI machine-learning repository and on three approximation functions datasets. The experimental results show that ISCA outperforms both BBO, GA and PSO for training FNNs in terms of converging speed and avoiding local minima.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Athanassopoulos, A.D., Curram, S.P.: A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. J. Oper. Res. Soc. 47(8), 1000–1016 (1996)
Bauer, P.W.: Recent developments in the econometric estimation of frontiers. J. Econom. 46(1–2), 39–56 (1990)
Benghalia, A., Boukachour, J., Boudebous, D.: Gestion du transfert interne de conteneurs: le cas du port du havre. Logistique Manage. 24(1), 57–69 (2016)
Bentaleb, F., Mabrouki, C., Semma, A.: Key performance indicators evaluation and performance measurement in dry port-seaport system: a multi criteria approach. J. ETA Marit. Sci. 3(2), 97–116 (2015)
Button, K., Chin, A., Kramberger, T.: Incorporating subjective elements into liners’ seaport choice assessments. Transp. Policy 44, 125–133 (2015)
Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)
Costa, A., Markellos, R.N.: Evaluating public transport efficiency with neural network models. Transp. Res. Part C Emerg. Technol. 5(5), 301–312 (1997)
de Lima, E.P., da Costa, S.E.G., de Faria, A.R.: Taking operations strategy into practice: developing a process for defining priorities and performance measures. Int. J. Prod. Econ. 122(1), 403–418 (2009)
Oliveira, G.F.D., Cariou, P.: The impact of competition on container port (in) efficiency. Transp. Res. Part A Policy Pract. 78, 124–133 (2015)
Hecht-Nielsen, R.: Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the IEEE International Conference on Neural Networks III, pp. 11–13. IEEE Press (1987)
Linstone, H.A., Turoff, M., et al.: The Delphi Method. Addison-Wesley, Reading (1975)
Mabrouki, C., Bentaleb, F., Mousrij, A.: A decision support methodology for risk management within a port terminal. Saf. Sci. 63, 124–132 (2014)
Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E 49(5), 4677 (1994)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Let a biogeography-based optimizer train your multi-layer perceptron. Inf. Sci. 269, 188–209 (2014)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Wang, S.: Adaptive non-parametric efficiency frontier analysis: a neural-network-based model. Comput. Oper. Res. 30(2), 279–295 (2003)
Wang, T., Cullinane, K.: The efficiency of European container terminals and implications for supply chain management. In: Haralambides, H.E. (ed.) Port Management, pp. 253–272. Springer (2015)
Woo, S.H., Pettit, S., Beresford, A.K.: Port evolution and performance in changing logistics environments. Marit. Econ. Logist. 13(3), 250–277 (2011)
Zhu, J.: Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, vol. 213. Springer, Switzerland (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fri, M., Douaioui, K., Lamii, N., Mabrouki, C., Semma, E.A. (2020). A DEA-Based Hybrid Framework to Evaluate the Performance of Port Container. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1104. Springer, Cham. https://doi.org/10.1007/978-3-030-36671-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-36671-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36670-4
Online ISBN: 978-3-030-36671-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)