基于AIS数据的航运物流港口调度优化研究

目的 针对宁波舟山港区的复杂航道水域与密集物流交通流，研究更加有效的调度方案，达成调度时间和等待时间最小化，即效率最大化。方法 分析宁波舟山港区航道的航行情况，提出交会处复杂航道水域存在的问题，以调度时间和等待时间最小为目标的多目标函数，建立复杂航道水域船舶调度模型。针对大量的船舶AIS数据，构建基于神经网络的航道水域调度模型，对不同类型、不同大小的船舶建立速度变化和船舶预测模型，实现对船舶调度状态的预测。设计以传统粒子群算法为基础的改良版船舶调度算法。结果 算法对模型求解表明，根据不同船长与间距可判别交通流拥挤程度进而对船舶进行调度。通过模型预测到可能产生拥挤，则应当选择小型船只走条帚门航道，大型船只走虾峙门航道，并且尽量避免产生拥堵。结论 使用该模型与算法可以有效地提升船舶调度效率，为复杂航运物流港口调度优化研究提供了一定理论基础。

Scheduling Optimization of Shipping Logistics Port Based on AIS Data

The work aims to study a more effective scheduling scheme to deal with the complex waterway waters and dense logistics traffic flow in Ningbo Zhoushan Port area, to minimize the scheduling time and waiting time, that is, to maximize the efficiency. The navigation situation of Zhoushan Port in Ningbo was analyzed. The problems existing in the complex waterway area were put forward. A multi-objective function was proposed to minimize dispatching time and waiting time, a ship dispatching model for the complex waterways was established. In view of a large number of AIS data for ships, a waterway area scheduling model based on neural network was constructed, and speed change and ship prediction models were established for ships of different types and sizes to realize the prediction of ship scheduling status. An improved ship scheduling algorithm based on traditional particle swarm optimization was designed. The results showed that the model was solved by the algorithm and the traffic congestion could be judged and ship scheduling could be carried out based on different captains and spacing. Once possible congestion was predicted through the model, small vessels should pass through from the Tiaozhou Men channel and large vessels should pass through from the Xiasi Men channel and avoid congestion as much as possible. The model and algorithm can effectively improve the efficiency of ship scheduling and provide a theoretical basis for optimization of complex scheduling in shipping logistics ports.