引用本文:陈斌,焦琳青,杨亚磊,张洋,王立文.复杂多约束条件下航班除冰延误机理及资源优化配置[J].控制理论与应用,2020,37(5):1069~1075.[点击复制]
CHEN Bin,JIAO Lin-qing,YANG Ya-lei,ZHANG Yang,WANG Li-wen.Flight deicing delay mechanism and resource optimization configuration under complex and multiple constraints[J].Control Theory and Technology,2020,37(5):1069~1075.[点击复制]
复杂多约束条件下航班除冰延误机理及资源优化配置
Flight deicing delay mechanism and resource optimization configuration under complex and multiple constraints
摘要点击 1922  全文点击 741  投稿时间：2019-05-30  修订日期：2019-09-29
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DOI编号  10.7641/CTA.2019.90408
  2020,37(5):1069-1075
中文关键词  除冰延误  资源配置  复杂多约束  粒子群算法  多属性决策
英文关键词  deicing delay  resource allocations  complex multi-constraint  particle swarm optimization algorithm  multiple attribute decision
基金项目  科技部科技支撑计划项目子课题项目(2012BAG04B02), 国家自然科学基金委员会— 中国民航局民航联合研究基金项目(U1933107)资助.
作者单位E-mail
陈斌* 航空地面特种设备民航研究基地 chenbindavid@163.com 
焦琳青 航空地面特种设备民航研究基地  
杨亚磊 航空地面特种设备民航研究基地  
张洋 航空地面特种设备民航研究基地  
王立文 航空地面特种设备民航研究基地  
中文摘要
      针对机场除冰运行存在的除冰航班延误频发、除冰资源使用粗放的问题, 提出了考虑航班延误机理的除 冰资源优化配置方法. 研究了多参数综合影响的除冰效率及航班动态随机环境下的除冰排队延误机理. 分析了除 冰运行过程的复杂多约束条件, 构建了以最小化除冰液消耗、除冰车需求和除冰延误架次为目标的飞机除冰资源 多目标优化配置模型. 利用自适应网格多目标粒子群算法求解模型并提出了基于多属性决策优化的除冰资源优化 配置方法. 除冰资源优化配置后除冰液消耗量、除冰车需求量、除冰延误架次相较于优化前分别降低了13.9%, 12.8%, 19.3%. 该方法为机场除冰运行资源优化提供了新思路.
英文摘要
      Aiming at the frequent delays of deicing flights and extensive use of deicing resources in airport deicing operation, an optimal allocation method of deicing resources considering flight delay mechanism is proposed. The deicing efficiency affected by multi-parameters and the deicing queuing delay mechanism under dynamic random flight environment are studied. The complex multi-constraints of deicing operation are analyzed, and a multi-objective optimal allocation model of aircraft deicing resources is established, which aims at minimizing deicing fluid consumption, deicing vehicle demand and delays of de-icing flights. An adapt grid algorithm based on multi-objective particle swarm optimization (AGA–MOPSO) is used to solve the model and an optimal allocation method of de-icing resources based on multi-attribute decision optimization is proposed. After optimizing the allocation of deicing resources, the deicing fluid consumption, deicing vehicle demand and de-icing flight delay were reduced by 13.9%, 12.8% and 19.3% respectively compared with those before optimization. This method provides a new idea for the optimization of airport deicing operation resources.