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针对多机场进场航班协同调度问题,以协同决策(collaborative decision making,CDM)理念为基础,在重点分析各航空公司之间排序公平性的基础上,提出了一种基于按时刻表分配(ration by schedule,RBS)公布顺序的离散化优化模型.该模型通过分析多机场终端区定位点和跑道双重约束,均衡各航空公司航班相对RBS次序位置变动数,实现了提高调度公平性、优化调度延误时间、减少航班改变位置架次的多目标优化.将模糊自修正多目标粒子群算法(FS-MOPSO)应用于模型进行求解计算,并对上海多机场终端区航班调度进行仿真模拟,结果表明:两机场的30架进场航班调度延误时间较传统先到先服务方案减少22.53%;各航空公司航班改变位置架次偏差值较单一以延误最优遗传算法仿真结果降低26.31%.
Based on the idea of collaborative decision making (CDM) and focusing on the fairness of ranking among airlines, aiming at the problem of multi-airport approach flight coordination scheduling, by schedule, RBS), this paper analyzes the position constraints of multi-airport terminal area and runway, and balances the change of RBS position relative to each airlines flight, so as to improve the fairness of scheduling and delay of optimization scheduling Time and reduce the number of flights to change the position of the multi-objective optimization.Fuzzy self-correcting multi-objective particle swarm optimization algorithm (FS-MOPSO) is applied to the model to solve the calculation, and flight scheduling in Shanghai multi-airport terminal area simulation results show that: two The dispatching delay time of 30 incoming flights at the airport was 22.53% lower than that of the traditional first-come-first-served solution. Compared with the delay of the optimal GA, simulation results of flight delays of all airlines were reduced by 26.31%.