Research on combination forecast of port cargo throughput based on time series and causality analysis

Purpose: The purpose of this paper is to develop a combined model composed of grey-forecast model and Logistic-growth-curve model to improve the accuracy of forecast model of cargo throughput for the port. The authors also use the existing data of a current port to verify the validity of the combined model. Design/methodology/approach: A literature review is undertaken to find the appropriate forecast model of cargo throughput for the port. Through researching the related forecast model, the authors put together the individual models which are significant to study further. Finally, the authors combine two individual models (grey-forecast model and Logistic-growth-curve model) into one combined model to forecast the port cargo throughput, and use the model to a physical port in China to testify the validity of the model. Findings: Test by the perceptional data of cargo throughput in the physical port, the results show that the combined model can obtain relatively higher forecast accuracy when it is not easy to find more information. Furthermore, the forecast made by the combined model are more accurate than any of the individual ones. Research limitations/implications: The study provided a new combined forecast model of cargo throughput with a relatively less information to improve the accuracy rate of the forecast. The limitation of the model is that it requires the cargo throughput of the port have an S-shaped change trend. Practical implications: This model is not limited by external conditions such as geographical, cultural. This model predicted the port cargo throughput of one real port in China in 2015, which provided some instructive guidance for the port development. Originality/value: This is the one of the study to improve the accuracy rate of the cargo throughput forecast with little information.Peer Reviewe

ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given