Delay Optimization for Airspace Capacity Management with Runtime and Equity Considerations

En route air traffic management is difficult and can benefit greatly from decision support tools. This paper presents a study of the efficiency and effectiveness of two practical approaches to real-time scheduling algorithms: a simple greedy scheduler and a well-studied optimal scheduler. A subset (region) of the National Airspace System is isolated to perform optimization on a manageable portion of the airspace. The schedulers are tested on realistic data sets representing traffic and conditions in the corridor between Chicago and New York area airports. In particular, the optimal scheduling of flights with both origin and destination in that corridor is considered, while reserving sufficient airspace for other air traffic. In a majority of cases, the greedy method provides sufficient (often optimal) results, while under difficult traffic and weather conditions, the optimal scheduler is worth the runtime requirements due to the inability of the greedy version to find satisfactory solutions. Further benefits of an optimal scheduler are demonstrated by incorporating the concept of equity or ‘fairness’ into the scheduling decision. Design choices in implementing equity amongst the various airlines are discussed and results demonstrating the utility of these choices are provided. Equity is easily implemented for the optimal scheduler but not the greedy, and does not require significant additional run time. Ultimately, it is shown that an equity-aware decision support tool for delay optimization can be developed to run in real-time and can benefit from incorporating more than one approach depending on the complexity of the scenario.