Efficient goods inspection demand at ports: a comparative forecasting approach
| Autor: | M.J. Jiménez-Come, Ignacio J. Turias, Juan‐Jesús Ruiz‐Aguilar, María Cerbán‐Jiménez, Jose‐Antonio Moscoso‐López |
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| Rok vydání: | 2017 |
| Předmět: |
Decision support system
021103 operations research Artificial neural network Operations research Computer science Strategy and Management Autoregressive conditional heteroskedasticity 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Port (computer networking) Computer Science Applications Support vector machine Nonlinear system Management of Technology and Innovation Linear regression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Autoregressive integrated moving average Business and International Management |
| Zdroj: | International Transactions in Operational Research. 26:1906-1934 |
| ISSN: | 1475-3995 0969-6016 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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