关键词: 舱位分配, 集疏运平衡, 空箱调运, 数据驱动, 穿梭运输

Abstract: Due to factors such as the variability of traffic volume between ports and the uncertainty of transportation demand, problems such as uneven distribution of empty containers and unbalanced collection and distribution have arisen, and the operating costs of liner companies have remained high. Reasonable distribution of container slots under limited resources and further improvement of the collecting-distributing transportation system of liner companies will help shipping companies to improve the utilization rate of resources and reduce the total cost.
To this end, in view of the mode and characteristics of container shuttle transportation, this thesis discusses the optimization of container shuttle transport slot allocation problem regarding the balance of collection and distribution and the repositioning of empty containers. Aiming at the uncertainty of transportation demand, an integrated forecasting model is constructed to predict the demand for shuttle transport, and the effectiveness of the model is verified based on historical data. The integrated forecasting model consists of SARIMA (Seasonal Autoregressive Integrated Moving Average Model), ASHW (Seasonal Holt-Winter Model) and machine learning algorithms such as LSTM (Long Short-Term Memory Network). On this basis, combined with the transportation demand of shippers and the operational demand of liner companies, the focus is on the balance of collection and distribution and the demand for empty container transfer. A mixed integer planning model is constructed for different container types to formulate transportation strategies according to different directions of collection and distribution, with the goal of minimizing the total cost. The model distinguishes between loaded and empty container transportation modes, restricting loaded containers to be transported only between feeder ports and hub ports, while allowing empty containers to be transferred between feeder ports and hub ports and among feeder ports. The exact solution of the example is obtained by using CPLEX solver. The solution is compared with the results of two scenarios restricting the transportation of empty containers only between feeder ports and hub ports and without considering the transfer of empty containers, respectively.
The research results show that the integrated prediction model constructed in this thesis can provide more accurate transportation demand data and strong data support for the slot allocation research. In addition, compared with the traditional loaded container fixed transportation mode, the total cost of the two routes can be optimized by 14.62% on average, compared with the transportation mode without considering the transfer of empty containers. Compared with the transportation mode without considering the transfer of empty containers, the total cost of the two routes can be optimized by 21.75% on average. Moreover, the sensitivity finding shows that the market demand of collecting-distributing transportation has a significant effect on the container slot allocation of liner companies. When market demand increases, Scenario 1 is more inclined to optimize slot utilization, empty container reposition are considered more frequently, and the total cost increase is reduced. Therefore, the slot allocation constructed in this thesis, which considers the balance of collection and distribution and the reposition of empty containers, can realize the optimal allocation of empty container resources for collection and distribution and promote the coordination and balance of cargo flow between feeding ports. It can reduce the transportation cost of liner companies to a certain extent while reducing the waste of resources.

Key words: container slot allocation, collecting-distributing transportation balancing, empty container repositioning, data-driven, shuttle transport