摘要
进港航班排序优化是提高航空公司的经济效益和空中交通流量管理的主要手段。作者在一定假设的条件下,以所有到达航班在机场终端总延误成本最小为目标函数,并在该函数中引进公平性惩罚因子,得到一个带有公平性惩罚因子的航班总延误成本最小进港排序模型。该模型用粒子群算法求解,作者给出了解法步骤。文中列出了两个实例。实例结果表明:在自然状况下,本文模型的总延迟成本显著地下降;与遗传算法的结果比较,两个方法都有效地降低了总延误成本,但是,本文方法的最大延迟顺序小于遗传算法的相应值,结果比较公平;此外,本文方法的计算量相对较小。
Arrival aircraft scheduling at the terminal area is a main measure for enhancing the economic efficiency of the air line company and the air traffic management. Under some suppositions, letting the minimum delay cost be the object function for all the arrival aircrafts at an air terminal, and considering the fair punishment factor, an arrival aircraft delay scheduling model was formed. The model was solved by adopting the particle swarm algorithm, and the solution steps were presented in the paper. Two examples were earried out. The results show that： in the nature situation, the general delay cost was obviously decreased with the model of this paper; comparing with the genetic algorithm, the two results get from them show a great decrease in the general delay cost, but the maximum delay order of the presented model is less than that of the genetic algorithm＇ s, so, the case is relatively fair; in addition to these, the calculation volume of the presented model is relatively small than that of the genetic algorithm＇s.
出处
《交通运输工程与信息学报》
2008年第4期57-62,共6页
Journal of Transportation Engineering and Information
关键词
进港航班
公平排序模型
流量管理
最小延误成本
粒子群
Landing aircrafts, fair scheduling, traffic flowmanagement, minimum delay cost,particle swarm optimization
