摘要
为了使机场停机位资源的利用更加合理、高效,系统地研究了机位优化分配问题。通过深入分析机坪管制程序与航空器地面运行特性,综合实际地面运行环境考虑了航空器机位大小匹配、特定航班进行机位分区停靠等约束限制,提出以近机位利用率、机位匹配效率最大化为目标的停机位多目标分配模型,设计了一种基于双目标的差分进化优化算法(Differential evolution, DE)对数学模型进行求解,并与遗传算法(Genetic Algorithm, GA)、粒子群算法(Particle swarm optimization, PSO)进行结果对比分析。利用成都双流机场的实际计划航班作为数据进行仿真验证,结果表明,DE算法相对于GA、PSO算法在近机位利用率指标中分别提升10.37%、10.38%;在机位匹配效率指标中分别分提升9.05%、12.02%。可见差分进化算法能有效提高近机位利用率与机位匹配效率。
In order to make the utilization of airport stand resources more reasonable and efficient,this paper systematically studied the optimal allocation of stands.Through in-depth analysis of apron control procedures and aircraft ground operation characteristics,and taking into account the actual ground operation environment,such constraints as aircraft stand size matching,and the parking of specific flights in different zones,a multi-objective allocation model for parking stands was proposed with the goal of maximizing the utilization rate of near stands and the efficiency of stand matching,A differential evolution optimization(DE)algorithm based on two objectives was designed to solve the mathematical model,and the results were compared with those of genetic algorithm(GA)and particle swarm optimization(PSO)..Taking advantage of the practical flights plan of Chengdu Shuangliu Airport as the data to conduct simulation verification,the results indicate that compared with GA and PSO algorithms,DE algorithm has increased by 10.37%and 10.38%respectively in the near-stand utilization index;In the aircraft seat matching efficiency index,it was increased by 9.05%and 12.02%respectively.It can be seen that the differential evolution algorithm can effectively improve the utilization rate of near aircraft and the efficiency of aircraft matching.
作者
王剑辉
刘继琳
WANG Jian-hui;LIU Ji-lin(Civil Aviation Flight University of China,GuanghanSichuan 618307,China)
出处
《计算机仿真》
北大核心
2023年第3期52-57,共6页
Computer Simulation
基金
2020年民航教育培训项目“省级教学实验平台提升建设”(0252036)
中国民用航空飞行学院科研基金(J2018-60)。
关键词
停机位分配
差分算法
机位匹配
多目标优化
Aircraft stands assignment
Differential evolution algorithm
Aircraft position match
Multi-objective optimization
