随机型协同时隙分配模型

为解决机场概率容量条件下的协同时隙分配问题，提出了随机型协同时隙分配策略. 根据机场容量预报的更新模式，分别建立了静态模型和动态模型，模型以各种概率容量情景下的总航班延误损失期望最小为功效性目标，以各种概率容量情景下的平均旅客延误时间期望最小为公平性目标，在满足有效性约束的基础上，为进场航班分配时隙；并采用多目标遗传算法NSGA-Ⅱ求解模型. 以某机场运行数据为例进行仿真验证，与随机型地面等待经典模型相比，本文模型实现了机场概率容量条件下时隙资源的功效、公平、有效分配.

Stochastic collaborative slot allocation models

To solve collaborative slot allocation with airport probability capacity, a stochastic collaborative slot allocation strategy was proposed. According to the form of airport capacity being updated, both static model and dynamic model were proposed. With the aim to minimize the expected total flight delay cost and expected average delay time of passengers, the principles of efficiency and equity were respectively established. On the basis of the constraints of effectiveness, slots were allocated to arrival flights. Then, multi-objective genetic algorithm NSGA-II was applied to solving the models. To test how well the models work in real world, a simulation test was performed based on the operation data of an airport. Test results show that, compared with typical models for stochastic ground delay policy, the models proposed could efficiently, equitably and effectively allocate slot with airport probability capacity.