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.
Highlights
Container transportation is a highly complicated process and involves numerous parties, necessitating close cooperation between ports, ships, shipping companies, and other relevant departments
This paper proposes mixed kernels, which can effectively improve the predictive performance of an support vector machine (SVM) model with weighted arrays of polynomial and radial basis kernel functions
This paper proposes a model for predicting the number of containers allocated to a container ship for one voyage
Summary
Container transportation is a highly complicated process and involves numerous parties, necessitating close cooperation between ports, ships, shipping companies, and other relevant departments. The number of allocated containers is influenced by the port of call, local GDP, port industrial structures, and collection and distribution systems; by shipping company-related factors such as the capacity of a company, inland turnaround time of containers, seasonal changes in cargo volume, and quantity of containers managed by the company; and by ship-related factors such as the transportation capacity of a single ship and the full-container-loading rate of the ship Each of these factors exerts distinct effects on the number of containers allocated to a container ship for one voyage; describing these factors by using. The novelty of this paper is that it proposes a mixed kernel SVM model for predicting the number of containers allocated to a container ship for a voyage
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