A Stochastic Discrete Event Simulation of Airline Network and Maintenance Operations

Abstract

The complexities associated with airline operations require operations planning to be divided into multiple problems solved sequentially by the respective departments: (1) network planning, and (2) maintenance planning.
Furthermore, airline operations take place in an intrinsically uncertain environment, which requires the development of robust plans and the use of effective recovery policies. Despite the close interaction of network and maintenance plans in this dynamic environment, it is current airline practice to evaluate plans from the two domains separately, thus not representing airline operations from an integrated perspective. To this end, a modular, stochastic, discrete event simulation model of airline operations named ANEMOS (Airline Network and Maintenance Operations Simulation) is presented in this paper. The model integrates network and maintenance operations dynamics, allowing the evaluation of plans, policies, and scenarios from both domains. The model is validated using data provided by a major European airline, and it is shown that the simulated results closely resemble the airline's historical operational performance. Finally, the model's capabilities are demonstrated with a case study investigating the effects of adding a second reserve aircraft to a fleet of fifty wide-body aircraft. Results show that the second reserve is capable of reducing cancellations by 55%, but the lost revenue associated with keeping an aircraft non-operational make it a very costly solution, with the avoided costs of disruptions quantified at 6.2% of the lost profit.