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Container flow forecasting through neural networks based on metaheuristics

Author

Listed:
  • M. Milenković

    (Zaragoza Logistics Center
    University of Belgrade)

  • N. Milosavljevic

    (State University of Novi Pazar)

  • N. Bojović

    (University of Belgrade)

  • S. Val

    (Zaragoza Logistics Center)

Abstract

In this paper we propose a fuzzy neural network prediction approach based on metaheuristics for container flow forecasting. The approach uses fuzzy if–then rules for selection between two different heuristics for developing neural network architecture, simulated annealing and genetic algorithm, respectively. These non-parametric models are compared with traditional parametric ARIMA technique. Time series composed from monthly container traffic observations for Port of Barcelona are used for model developing and testing. Models are compared based on the most important criteria for performance evaluation and for each of the data sets (total container traffic, loaded, unloaded, transit and empty) the appropriate model is selected.

Suggested Citation

  • M. Milenković & N. Milosavljevic & N. Bojović & S. Val, 2021. "Container flow forecasting through neural networks based on metaheuristics," Operational Research, Springer, vol. 21(2), pages 965-997, June.
    • Handle: RePEc:spr:operea:v:21:y:2021:i:2:d:10.1007_s12351-019-00477-1
    DOI: 10.1007/s12351-019-00477-1
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    References listed on IDEAS

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    Cited by:

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    2. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
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    5. Anqiang Huang & Xinjun Liu & Changrui Rao & Yi Zhang & Yifan He, 2022. "A New Container Throughput Forecasting Paradigm under COVID-19," Sustainability, MDPI, vol. 14(5), pages 1-20, March.

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