A hybrid optimization-simulation approach for itinerary generation

Abstract

Simulation models are often employed to evaluate projected future performance of the US National Airspace System (NAS) for the purposes of long-term aviation investment planning and performance benchmarking. The future schedules are developed as input for simulation to represent the forecast for airport operations. The itinerary structure of a future schedule has a significant impact on the operational characteristics such as schedule peaks and aircraft utilization. Itinerary generation algorithms seeking to maximize the aircraft utilization may cause schedule smoothing or de-peaking which is undesirable for airlines wishing to maintain their schedule peaks. In addition, itineraries with high aircraft utilization are likely to have more propagated delay. To achieve a certain level of balance between aircraft utilization and a desired level of schedule peaks and delay performance is a complex task for the itinerary generation process especially when the entire NAS is involved. This paper proposes a new method for creating future itineraries based on a hybrid solution of simulation and optimization techniques. The Mixed-Integer Programming (MIP) technique is used to solve the itinerary generation problem with the objective to maximize the aircraft utilization of the itinerary structure of the flights. The simulation technique is used to evaluate the performance of the NAS in terms of delay with the generated itineraries from the MIP solution. Based on the output of the simulation, the MIP model will be modified by adjusting its parameters and solved again. This iterative process will continue until the desired result is obtained from the simulation. This paper also provides a quantitative analysis to demonstrate a trade-off between the de-peaking strategies that minimize the number of aircraft in service and the banking strategies that maintain schedule banks.