基于遗传粒子群算法的停机位分配问题研究  

Research on Aircraft stands Assignment Problem Based on Genetic Particle Swarm Algorithm

作　　者：向征[1] 储同 周鼎凯 孙赫阳 XIANG Zheng;CHU Tong;ZHOU Ding-kai;SUN He-yang(Civil Aviation Flight University of China,Guanghan Sichuan 618307,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,四川广汉618307

出　　处：《计算机仿真》2024年第11期58-62,379,共6页Computer Simulation

基　　金：中国民用航空飞行学院国家级、省级一流本科课程建设项目(H2022001);中国民用航空飞行学院科研项目(J2021-082);中央高校基本科研业务费专项资金资助(24CAFUC03050)。

摘　　要：为保证机场停机位资源的合理分配和高效利用,通过分析停机位分配过程中的安全运行规则,考虑了以航空器机位大小匹配、同一机位和相邻机位安全时间间隔等约束条件,建立了最小化远机位数量和最小化近机位空闲时间的多目标优化模型。提出了一种将遗传算法(GA)和粒子群算法(PSO)优势相结合的遗传粒子群算法(GAPSO),同时改进了自适应度对粒子速度的更新限制,提供了一种新的停机位分配与优化思路。仿真结果表明,GAPSO算法在远机位分配数量和近机位的空闲时间方面表现更优,同时还展现出更快的收敛速度和更强的全局寻优能力,从而提高航空运输的效率,对机场管理和停机位资源分配具有实际意义。In order to ensure the rational allocation and efficient utilization of airport apron resources,this study analyzes the safety operational rules in the apron allocation process.It considers constraints such as matching aircraft stand sizes,safety time intervals between the same apron and adjacent aprons.A multi-objective optimization model is developed to minimize the number of remote aprons and minimize the idle time of nearby aprons.A novel approach to apron allocation and optimization is proposed,combining the advantages of Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)into a Genetic Particle Swarm Optimization(GAPSO)algorithm.The adaptive limitation on the particle velocity update is also improved.The simulation results demonstrate that the GAPSO algorithm performs better in terms of remote apron allocation and idle time of nearby aprons.It exhibits faster convergence speed and stronger global optimization ability,thereby enhancing the efficiency of aviation transportation and providing practical significance to airport management and apron resource allocation.

关 键 词：停机位分配 遗传粒子群算法 机位匹配 混合算法 

分 类 号：V351.11[航空宇航科学与技术—人机与环境工程]