| We present a stochastic model of the daily operations of an airline. Its primary purpose is to evaluate plans, such as crew schedules, as well as recovery policies in a random environment. We describe the structure of the stochastic model, sources of disruptions, recovery policies, and performance measures. Then, we describe SimAir—our simulation implementation of the stochastic model, and we give computational results.; During disruptive events, airline decision makers must reschedule flight legs, and reroute aircraft, pilots, and passengers. We present an optimization model that reschedules legs and reroutes aircraft by minimizing an objective function involving rerouting and cancellation costs. We develop a heuristic for selecting which aircraft are rerouted, and we provide proof-of-concept by evaluating our model using SimAir. We revise the model to minimize crew and passenger disruptions.; Airline decision makers cancel flights in operations because of disruptions. When canceling a flight, they usually cancel a cycle, a sequence of flights that begins and ends at the same airport. Consequently, a fleet assignment and aircraft rotation with many short cycles is frequently less sensitive to a flight cancellation than one with only a few short cycles. We present a robust fleet assignment model that embeds many short cycles within a solution. We demonstrate the robustness of solutions to such models using SimAir and the aircraft recovery optimization model.; Finally, we give future directions for the study of airline recovery policies and planning under uncertainty. |
