An optimization model for sample day selection in NAS-wide modeling studies

Abstract

Future flight Schedules are generated based on air traffic demand forecast for the purpose of aviation planning and performance analysis studies. A selection process needs to be designed and implemented by sampling historical operational data for each fiscal quarter and choosing representative days that best reflect seasonality in terms of a given set of performance metrics. We propose an optimization based solution method for 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. An efficient solution algorithm has been implemented using the CPLEX MIP solver. Experiments have been conducted with a wide range of flight data from the recent years. The results from the MIP method provided robust solutions for the sample day selection problem. It is also shown that the method is quite flexible to incorporate additional constraints based on expert knowledge.