油轮码头泊位停留时间预测的系统动态方法
Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach
摘要：
在数字化和海运量不断增加的趋势下，船舶泊位停留时间的预测是航运大数据时代运筹学和调度优化问题的需要，对港口效率和海运物流的提升具有重要意义。本研究提出一种系统化、动态化的预测油船码头泊位停留时间的方法。该方法的创新之处在于：（1）采用了多方面的数据源，包括油轮码头的货物作业数据、自动识别系统（AIS）的时间序列数据等;（2）创新性地通过数据分析和信息抽取将泊位停留过程分解为多个模块，并据此开发了实际操作场景。3)在先验数据分析和信息抽取的基础上，采用回归和分解分布两种方法建立了泊位停留时间的预测模型。在四种不同的动态场景下对模型进行了评估，其中指定货物在两个不同的码头之间。评估结果表明，该方法预测泊位停留时间的准确率达到98.81%，并具有动态预测泊位停留时间的能力。该模型可以应用于短期试点预订或调度优化在合理的时间范围内，港口智能化和物流效率的进步。
英文摘要：
Given the trend of digitization and increasing number of maritime transport, prediction of vessel berth stay has been triggered for requirements of operation research and scheduling optimization problem in the era of maritime big data, which takes a significant part in port efficiency and maritime logistics enhancement. This study proposes a systematic and dynamic approach of predicting berth stay for tanker terminals. The approach covers three innovative aspects: 1) Data source employed is multi-faceted, including cargo operation data from tanker terminals, time-series data from automatic identification system (AIS), etc. 2) The process of berth stay is decomposed into multiple blocks according to data analysis and information extraction innovatively, and practical operation scenarios are also developed accordingly. 3) The predictive models of berth stay are developed on the basis of prior data analysis and information extraction under two methods, including regression and decomposed distribution. The models are evaluated under four dynamic scenarios with certain designated cargoes among two different terminals. The evaluation results show that the proposed approach can predict berth stay with the accuracy up to 98.81% validated by historical baselines, and also demonstrate the proposed approach has dynamic capability of predicting berth stay among the scenarios. The model may be potentially applied for short-term pilot-booking or scheduling optimizations within a reasonable time frame for advancement of port intelligence and logistics efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
