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Container flow forecasting through neural networks based on metaheuristics

M. Milenković (), N. Milosavljevic, N. Bojović and S. Val
Additional contact information
M. Milenković: Zaragoza Logistics Center
N. Milosavljevic: State University of Novi Pazar
N. Bojović: University of Belgrade
S. Val: Zaragoza Logistics Center

Operational Research, 2021, vol. 21, issue 2, No 8, 965-997

Abstract: 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.

Keywords: Neural networks; Simulated annealing; Genetic algorithm; ARIMA; Container; Forecasting (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12351-019-00477-1

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