论文标题
一种新的Simheuristic方法，用于随机跑道调度
A New Simheuristic Approach for Stochastic Runway Scheduling
论文作者
论文摘要
我们考虑一个随机，动态的跑道调度问题，涉及飞机登陆在单个跑道上的登陆。测序决策是通过了解到达机场的所有飞机的估计到达时间（ETA）做出的，这些ETA会根据连续的时间随机过程而有所不同。连续跑道着陆之间的时间分离是通过序列依赖性的Erlang分布来建模的，并受到天气条件的影响，天气条件也随着时间的流逝而不断发展。最终的多阶段优化问题使用精确的方法很难进行，我们提出了一种新型的Simheuristic方法，基于在高维随机环境中类似于可变邻域搜索（VNS）的方法。使用98,000多个到达希思罗机场的飞行跟踪数据进行校准。数值实验的结果表明，我们提出的simheuristic算法优于基于确定性预测的替代方案，在广泛的参数值下的替代方案，当基础随机过程变得更加波动时，当单个飞行的准时要求更大时，在目标函数中的重量更大时，就会看到最大的好处。
We consider a stochastic, dynamic runway scheduling problem involving aircraft landings on a single runway. Sequencing decisions are made with knowledge of the estimated arrival times (ETAs) of all aircraft due to arrive at the airport, and these ETAs vary according to continuous-time stochastic processes. Time separations between consecutive runway landings are modeled via sequence-dependent Erlang distributions and are affected by weather conditions, which also evolve continuously over time. The resulting multi-stage optimization problem is intractable using exact methods and we propose a novel simheuristic approach, based on the application of methods analogous to variable neighborhood search (VNS) in a high-dimensional stochastic environment. Our model is calibrated using flight tracking data for over 98,000 arrivals at Heathrow Airport. Results from numerical experiments indicate that our proposed simheuristic algorithm outperforms an alternative based on deterministic forecasts under a wide range of parameter values, with the largest benefits being seen when the underlying stochastic processes become more volatile and also when the on-time requirements of individual flights are given greater weight in the objective function.
