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Container flow forecasting through neural networks based on metaheuristics

Operational Research volume 21, pages 965–997 (2021)Cite this article

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.

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Acknowledgements

Authors would like to express their gratitude to Prof. Nikolaos Fragkiskos Matsatsinis (Editor) and two anonymous reviewers for their very useful suggestions which significantly improved this paper. The paper is supported by the Serbian Ministry of Education and Science (Project III44006 and I36022) and the project “Clusters 2.0: Open network of hyper connected logistics clusters towards Physical Internet” which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 723265.

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Authors and Affiliations

  1. Zaragoza Logistics Center, Zaragoza, Spain

    M. Milenković & S. Val

  2. Division for Management in Railway, Rolling Stock and Traction, The Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia

    M. Milenković & N. Bojović

  3. Department for Mathematical Sciences, State University of Novi Pazar, Novi Pazar, Serbia

    N. Milosavljevic

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  1. M. Milenković
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  2. N. Milosavljevic
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  3. N. Bojović
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Correspondence to M. Milenković.

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Milenković, M., Milosavljevic, N., Bojović, N. et al. Container flow forecasting through neural networks based on metaheuristics. Oper Res Int J 21, 965–997 (2021). https://doi.org/10.1007/s12351-019-00477-1

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  • DOI: https://doi.org/10.1007/s12351-019-00477-1

Keywords

  • Neural networks
  • Simulated annealing
  • Genetic algorithm
  • ARIMA
  • Container
  • Forecasting