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基于FS-MOPSO的多机场终端区协同航班调度策略

王湛,  吴艺

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文章导航 > 西南交通大学学报  > 2017 >  30(1): 179-185.
王湛, 吴艺. 基于FS-MOPSO的多机场终端区协同航班调度策略[J]. 西南交通大学学报, 2017, 30(1): 179-185. doi: 10.3969/j.issn.0258-2724.2017.01.025
引用本文: 王湛, 吴艺. 基于FS-MOPSO的多机场终端区协同航班调度策略[J]. 西南交通大学学报, 2017, 30(1): 179-185. doi: 10.3969/j.issn.0258-2724.2017.01.025
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WANG Zhan, WU Yi. Collaborative Aircrafts Scheduling Strategy in Metroplex Terminal Area Based on FS-MOPSO[J]. Journal of Southwest Jiaotong University, 2017, 30(1): 179-185. doi: 10.3969/j.issn.0258-2724.2017.01.025
Citation: WANG Zhan, WU Yi. Collaborative Aircrafts Scheduling Strategy in Metroplex Terminal Area Based on FS-MOPSO[J]. Journal of Southwest Jiaotong University, 2017, 30(1): 179-185. doi: 10.3969/j.issn.0258-2724.2017.01.025
shu

基于FS-MOPSO的多机场终端区协同航班调度策略

doi: 10.3969/j.issn.0258-2724.2017.01.025
基金项目: 

江苏省自然科学基金资助项目（Bk20130821）

详细信息
    作者简介:

    王湛(1982-),女,博士,研究方向为空中交通管理与规划、软件性能保持,电话:13851635401,E-mail:wangzhan82512@163.com

Collaborative Aircrafts Scheduling Strategy in Metroplex Terminal Area Based on FS-MOPSO

    摘要
  • 摘要: 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%.

     

    Abstract: To solve the collaborative scheduling problem for arrival aircrafts in metroplex terminals, a discrete optimization model was proposed under ration-by-schedule (RBS). The model focuses on improving scheduling fairness between airlines based on collaborative decision-making (CDM) to seek a best scheduling strategy. By analyzing the fix and runway double restraints and keeping the RBS order as far as possible by minimizing the number of each flight's reversals, the model realized the muilt-objectives of improving the scheduling fairness and minimizing the total delay and the total amount of flights overtaking. In addition, fuzzy self-correction multi-objectives particle swarm optimization (FS-MOPSO) was used to solve the model. As a case study, the scheduling problem for arrival aircrafts in Shanghai metroplex was simulated by the proposed method to verify its effectiveness. The results show that the collaborative scheduling strategy can reduce the total delay time by 22.53% compared to the traditional strategy, and the decrease the variance of reversals by 26.31% compared to the genetic algorithm aimed at minimizing the delay time only.

     

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出版历程
  • 收稿日期:  2015-05-18
  • 刊出日期:  2017-02-25

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