基于指标函数的舰载机机队回收调度优化研究

由于复飞逃逸、飞行故障等因素随机发生, 传统静态调度方法难以有效解决动态随机调度问题。对此，提出一种基于飞机优先序指标函数的蒙特卡罗模拟-差分进化搜索的实时调度算法。在离线模拟回收环境下，由算法完成基于随机模型的指标函数系数优化，后由该指标函数实现对机队优先序的实时评估排序，进而完成回收在线动态调度。仿真结果表明所提算法能有效解决回收调度问题。在相同机队初始输入下, 目标值呈现较好统计特性, 其期望值能够快速收敛到一定范围, 且无复飞情况下着舰时间窗目标呈显著正态性; 不同回收机队输入对指标系数最优值的散布较小, 表明指标函数评估优先序具有较好的通用性和有效性; 模拟回收调度发现, 着舰成功率的提高将显著降低机队复飞次数, 提高回收效率。

Research on optimization of carrier-based aircraft fleetrecovery scheduling based on index function

Due to the random occurrence of factors such as go-around escape and flight failure, the traditional static scheduling method is difficult to effectively solve the dynamic random scheduling problem. Aiming at this problem, the real-time scheduling algorithm of the Monte Carlo simulation and differential evolution search based on an index function to complete aircraft priority evaluation is propased. In the offline simulated recovery environment, the optimal index function coefficients are searching by the algorithm based on the stochastic model, by which the real-time evaluation-sorting of the fleet landing priority is achieved, and then generates dynamic scheduling plan online.The simulation results showed the algorithm could effectively solve the problem. In same initial input of the fleet, the expectation of target value presenting good statistical characteristics could converge to a certain range quickly, and the target value of the landing time window was significantly normal without a missed approach; the distribution of the optimal values of the index coefficients among different recovery fleet inputs was small, which indicated that the index function evaluating priority had good generality and effectiveness; the simulated recovery dispatch experiment showed that the increase in the success rate of landing would significantly reduce the number of missed approach and improve recovery efficiency.