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An Optimization-Based Sample Day Selection Algorithm for Future Schedule Generation

机译：基于优化的样本日选择算法，用于未来计划的生成

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Future flight Schedules are generated using air traffic growth forecasts along with a set of baseline schedules. The baseline schedules are usually selected by sampling historical operational data for a fiscal year and choosing representative days that best reflect seasonality in terms of a given set of performance metrics. Larger sample sizes capture more accurate trends in the NAS at the expense of the computer processing time of other important elements of the model process. This trade-off is evaluated each year based on known information on computer run-time and other performance requirements of the modeling community. To maximize accuracy with a minimal sample, we propose an optimization based method for solving the sample day selection problem, which is formulated as a Mixed Integer Program (MIP). The objective of the MIP is to minimize the weighted difference between the true population and the sample to be selected in terms of the defined metrics subject to a set of constraints including the sample size limit, coverage requirements and other desired properties. This paper presents two solution algorithms which have been implemented using the CPLEX MIP solver. A standard MIP formulation is first presented followed by a decomposition formulation which partitions the problem into smaller parts in order to reduce the computation time required for larger day selection exercises.
机译：将来的航班时刻表是使用空中交通流量增长预测以及一组基准时刻表生成的。通常通过对一个会计年度的历史操作数据进行采样并选择最能反映给定性能指标集的季节性的代表性日期来选择基线计划。较大的样本数量会以NAS处理模型过程中其他重要元素的计算机处理时间为代价，从而在NAS中捕获更准确的趋势。每年根据有关计算机运行时的已知信息和建模社区的其他性能要求，对这种折衷进行评估。为了用最少的样本最大化准确性，我们提出了一种基于优化的方法来解决样本日选择问题，该方法被公式化为混合整数程序（MIP）。 MIP的目标是根据定义的度量标准，使真实种群与要选择的样本之间的加权差异最小，该定义的度量标准受一组约束（包括样本量限制，覆盖范围要求和其他所需属性）的约束。本文介绍了使用CPLEX MIP求解器实现的两种解决方案算法。首先介绍标准的MIP公式，然后介绍分解公式，该分解公式将问题分成较小的部分，以减少较大日期选择练习所需的计算时间。

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