Dynamic departure pushback control at airports: Part A—Linear penalty‐based algorithms and policies

J. Desai, Guan Lian, S. Srivathsan
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Abstract

Airport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.
机场动态离港推回控制:A 部分--基于线性惩罚的算法和策略
机场地面拥堵会导致滑行离场时间过长,从而导致燃料消耗成本增加以及温室气体排放过多。为了抑制这种不良现象,本文提出了一种新的基于惩罚的动态离港后推控制(PDPC)策略,该策略采用线性惩罚函数,不仅依赖于滑行道排队限制,还依赖于当前排队长度,以配比机场后推频率,并将滑行道排队时间与登机口滞留延误进行交换,从而使总运行成本(燃油消耗和登机口滞留成本)最小化。利用北京首都国际机场(PEK)的数据,比较了四种不同的离港后推控制策略,即:(i) 无控制(基准情况);(ii) 传统控制;(iii) 具有恒定滑行道限制的 PDPC;(iv) 具有变化滑行道限制的 PDPC。详细的蒙特卡罗模拟展示了总成本函数对各种问题参数的敏感性,结果表明,与基准情况相比,采用 PDPC 政策可使总运营成本降低 42%,燃料消耗(千克)降低 68%。为了从分析上巩固这些模拟结果,我们还开发了一种基于马尔可夫链的迭代优化算法,用于估算使总成本函数最小化的推回率和滑行道队列限制的最佳值。这种分析框架在缺乏可靠机场数据的情况下非常有用,因为它只需要对历史推回请求率和滑行道服务时间进行估算,同时还能密切反映模拟结果。我们的蒙特卡罗模拟和马尔可夫链优化模型验证了所建议的 PDPC 政策的优势和影响,并利用 PEK 机场的数据证明了其在减少机场地面拥堵方面的实际功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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