基于模糊关键路径法的飞机总周转时间估计

IF 1.3 Q4 ENGINEERING, INDUSTRIAL
E. Asadi, H. Fricke
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引用次数: 1

摘要

机场协同决策(ACDM)飞行调度计划依赖于对最佳和可靠的飞机周转时间的准确预测,这是最难完成的事情之一。为了考虑随机性和模糊性对周转过程持续时间的影响,本文将良好拟合子过程的概率分布转化为一个累积密度函数,即模糊隶属函数(FMF),然后利用拟合优度确定周转子过程的模糊隶属度等级。采用模糊关键路径法(FCPM)计算周转时间,该方法是模糊集理论和关键路径法的结合。为了验证这个估计,我们使用历史数据创建了FMF,并将其与基于FCPM的周转时间进行了比较。采用线性回归模型考察了到货延迟与基于fcpm的周转过程之间的关系。我们还使用历史数据,利用2017年夏季法兰克福机场的数据生成不同到达延误的模糊集,并得出延误与基于fcpm的周转过程正相关的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aircraft total turnaround time estimation using fuzzy critical path method
Airport Collaborative Decision Making (ACDM) flight scheduling plans rely on accurate predictions of both optimal and reliable aircraft turnaround times, which is one of the most difficult things to complete. In order to account for the effects of randomness and fuzziness on the turnaround process duration, this paper deals with transforming the probability distribution of well-fitted sub-processes into a cumulative density function, which is equivalent to the fuzzy membership function (FMF), and then using goodness-of-fit to determine the fuzzy membership grade of each turnaround sub-process. The turnaround time is calculated using the fuzzy critical path method (FCPM), which is a combination of the Critical Path Method (CPM) and fuzzy set theory. In order to verify this estimate, we created the FMF using historical data and compared it to turnaround times based on FCPM. A linear regression model is used to examine the relationship between arrival delays and the FCPM-based turnaround process. We also use historical data to generate fuzzy sets of different arrival delays using Frankfurt airport data from the summer of 2017 and conclude that delays are positively correlated with the FCPM-based turnaround process.
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来源期刊
CiteScore
3.70
自引率
5.90%
发文量
16
审稿时长
16 weeks
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