Evaluate the Performance of Port Container Using an Hybrid Framework

  • Mouhsene FriEmail author
  • Kaoutar Douaioui
  • Nabil Lamii
  • Charif Mabrouki
  • El Alami Semma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076)


This work intends to integrate feedforward neural network (FNN) 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 crow search algorithm (ICSA) is employed as a new method for training FNNs to determine the efficiency scores. In ICSA, the so-called Levy flights are used to enhance the convergence rate of CSA 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 ICSA 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 ICSA outperforms both BBO, GA and PSO for training FNNs in terms of converging speed and avoiding local minima.


Port container terminal (PCT) Performance measurement system (PMS) Data envelopment analysis (DEA) Feedforward neural network (FNN) Levy flights Crow search algorithm 


  1. 1.
    Taylor, I., Smith, K.: United Nations Conference on Trade and Development (UNCTAD). Routledge (2007)Google Scholar
  2. 2.
    Bentaleb, F., Mabrouki, C., Semma, A.: Key performance indicators evaluation and performance measurement in dry port-seaport system 58; a multi criteria approach. J. ETA Maritime Sci. 3(2), 97–116 (2015)Google Scholar
  3. 3.
    Bentaleb, F., Fri, M., Mabrouki, C., Semma, E.: Dry port-seaport system development: application of the product life cycle theory. J. Transp. Logistics 1, 116–128, 10 (2016)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Costa, Á., Markellos, R.N. : Evaluating public transport efficiency with neural network models. Transp. Res. Part C Emerg. Technol. 5(5), 301–312 (1997)Google Scholar
  6. 6.
    Bauer, P.W.: Recent developments in the econometric estimation of frontiers. J. Econ. 46(1–2), 39–56 (1990)Google Scholar
  7. 7.
    Wang, S.: Adaptive non-parametric efficiency frontier analysis: a neural-network-based model. Comput. Oper. Res. 30(2), 279–295 (2003)CrossRefGoogle Scholar
  8. 8.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)Google Scholar
  9. 9.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Let a biogeography-based optimizer train your multi-layer perceptron. Inf. Sci. 269, 188–209 (2014)Google Scholar
  10. 10.
    Zhu, J.: Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets, vol. 213. Springer (2014)Google Scholar
  11. 11.
    Mabrouki, C., Bentaleb, F., Mousrij, A.: A decision support methodology for risk management within a port terminal. Saf. Sci. 63, 124–132 (2014)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)Google Scholar
  14. 14.
    Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)CrossRefGoogle Scholar
  15. 15.
    Oliva, D., Hinojosa, S., Cuevas, E., Pajares, G., Avalos, O., Gálvez, J.: Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst. Appl. 79, 164–180 (2017)Google Scholar
  16. 16.
    Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of levy stable stochastic processes. Phys. Rev. E 49(5), 4677 (1994)Google Scholar
  17. 17.
    Linstone, H.A., Turoff, M., et al.: The Delphi Method. Addison-Wesley Reading, MA (1975)Google Scholar
  18. 18.
    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)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mouhsene Fri
    • 1
    • 2
    Email author
  • Kaoutar Douaioui
    • 1
  • Nabil Lamii
    • 1
  • Charif Mabrouki
    • 1
  • El Alami Semma
    • 1
  1. 1.LMII-Faculty of Sciences and TechnologyHassan 1st UniversitySettatMorocco
  2. 2.CELOG-ESITHCasablancaMorocco

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