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报告题目：Aircraft timing, sequencing and routing optimization in terminal control areas during disturbances

报告人简介：Andrea D'Ariano，教授，罗马第三大学(Roma Tre University)土木工程、计算机科学与航空技术工程系。他在罗马第三大学先后获得计算机科学自动化与管理工程的学士和硕士学位。2003年11月，他加入荷兰代尔夫特理工大学(Delft University of Technology)土木工程与地球科学学院的交通与规划系，并成为TRAIL研究学院的成员。2008年4月，在Ingo A. Hansen授的指导下，顺利获得博士学位。D'Ariano教授曾担任欧盟委员会及多个国家研究基金的专家与报告员。他是意大利运筹学协会(AIRO)“公共交通与共享出行优化”分会的协调员，同时担任多个国际知名期刊(如Transportation Research Part B、C、E)的副主编，并积极参与重要学术会议(如IEEE智能交通系统国际会议)的组织工作。他的主要研究方向是调度与路径规划算法的设计与开发，研究成果广泛应用于公共交通和物流领域。

报告内容：Intelligent decision support tools for aircraft monitoring and control are required in a busy Terminal Control Area (TCA). The problem of effectively managing TCA operations is particularly challenging, since there is a significant growth of traffic demand and the TCAs are becoming a bottleneck of the air traffic control system. The resulting increase in airport congestion, economic and environmental penalties can be measured in terms of performance indicators related to take-off and landing operations, e.g., aircraft delays, travel times and fuel consumption.

This talk addresses the real-time aircraft routing and scheduling problem at congested TCAs by optimizing the above-mentioned performance indicators. The mathematical formulation of this problem requires to consider the following key aspects: the aircraft trajectory and routing should be accurately predicted and optimized, the safety rules between consecutive aircraft need to be precisely modelled in each air/ground TCA resource, the aircraft timing and ordering decisions have to be taken in a short time. We discuss solving methods to integrate these modelling features and performance indicators.  The proposed framework computes an initial trajectory for each aircraft, proposes a feasible aircraft schedule with pre-defined routes, and improves this schedule by rerouting some aircraft in the TCA. Computational experiments are performed on mixed-mode runway instances from Roma Fiumicino and Milano Malpensa. The disturbed traffic situations are generated by simulating multiple delayed aircraft and temporarily disrupted runways. The optimization-based approaches improve the solutions significantly compared to practical scheduling rules. However, trade-offs emerge in picking the right approach and paradigms for practical implementation.