A Forecast Model of the Number of Containers for Containership Voyage

• Published in: Algorithms Vol. 11; no. 12; p. 193
• Main Authors: Wang, Yuchuang, Shi, Guoyou, Sun, Xiaotong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: Cargo ships; Container ships; container transportation; Containers; Genetic algorithms; GRA; Mathematical models; Model accuracy; Neural networks; Ports; prediction of voyage container volume; Stowage (onboard equipment); Support vector machines; SVM; Transport buildings, stations and terminals
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a11120193

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.