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Ruiz-Aguilar, Juan-Jesús; Turias, Ignacio; Moscoso-López, Jose-Antonio; Jiménez-Come, María-Jesús; Cerbán-Jiménez, María

Efficient goods inspection demand at ports: a comparative forecasting approach. (English) Zbl 07766378

Int. Trans. Oper. Res. 26, No. 5, 1906-1934 (2019).
Summary: A high number of freight inspections carried out at Border Inspection Posts (BIPs) of ports could lead to significant time delays and congestion problems within the port system, decreasing the efficiency of the port. Therefore, this work is focused on achieving the most accurate prediction of the daily number of goods subject to inspection at BIPs. Five prediction methods were used for this aim: multiple linear regression, seasonal autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, artificial neural networks, and support vector regression models. Several nonlinear tests were used to study the nature of the time series and the best method was obtained by the comparison of the prediction results based on performance indexes that provide the goodness-of-fit. The result of this study may become a supporting tool for the prediction of the number of goods subject to inspection in BIPs of other international seaports or airports.
{© 2017 The Authors. International Transactions in Operational Research © 2017 International Federation of Operational Research Societies}

Cited in 1 Document

MSC:

90-XX Operations research, mathematical programming

Keywords:

inspection forecasting; seasonal ARIMA; GARCH; artificial neural networks; support vector regression; decision support systems
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