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Algorithms 2018, 11(12), 193; https://doi.org/10.3390/a11120193

A Forecast Model of the Number of Containers for Containership Voyage

1
Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Navigation College, Dalian Maritime University, Dalian 116026, China
2
Shipping Economics and Management College, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Received: 20 October 2018 / Revised: 23 November 2018 / Accepted: 26 November 2018 / Published: 28 November 2018
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
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Abstract

Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage. View Full-Text
Keywords: container transportation; prediction of voyage container volume; SVM; GRA container transportation; prediction of voyage container volume; SVM; GRA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Y.; Shi, G.; Sun, X. A Forecast Model of the Number of Containers for Containership Voyage. Algorithms 2018, 11, 193.

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