基于遗传模拟退火算法的航班进离场调度  

Genetic Simulated Annealing Algorithms for Aircrafts Take-off and Landing with Multiple Objectives

作　　者：张书琴[1] 姜雨[1] 夏洪山[1] 刘照明[1,2] 

机构地区：[1]南京航空航天大学民航学院,江苏南京210016 [2]中船第九设计研究院工程有限公司,上海200063

出　　处：《航空计算技术》2016年第5期4-8,共5页Aeronautical Computing Technique

基　　金：国家自然科学基金项目资助(U1333117);国家博士后科学基金资助项目(2012M511275)

摘　　要：基于航班延误成本构成的复杂性,惩罚航空器单位时间延误成本以区分续航航班与非续航航班,且推导出与航班类型直接相关的续航航班单位时间延误成本表达式。建立了以航班总延误成本及跑道调度时间跨度最小的多目标跑道调度模型,并用遗传模拟退火算法求解模型。以国内某大型机场的两条近距平行跑道调度为例对算法进行验证,实验结果表明,运用遗传模拟退火算法求解多目标跑道调度问题,可显著提高航班延误成本分布的均衡性,且程序收敛性较强,具有很好的实用性。Multi-objective runway scheduling model was established to deal with some runways schedu- ling with genetic simulated annealing algorithms, whose objections were the minimum flight delay cost and the minimum span of operation time. Based on the complexity of flight delay cost components, the paper made a distinction between continuing flight and non- continuing flight＇s cost per unit by penalizing air- craft delay cost per unit, and the expression of continuing flight delay cost per unit being relevant to flight typewas derived. Finally, the two closely parallel runways of one hub domestic airport were introduced to verify the algorithm and model. The results show that using genetic simulated annealing algorithm to solve multi- objective scheduling problem can greatly enhance the balance of flight delay cost distribution, and the program has stronger convergence than genetic algorithm making it has a strong timeliness.

关 键 词：航空运输 跑道调度 遗传模拟退火算法 多目标优化 延误成本均衡 

分 类 号：V355[航空宇航科学与技术—人机与环境工程] O242[理学—计算数学]