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Airline scheduling consists of several major听planning challenges: building a flight schedule,assigning aircraft to each flight leg, assigning a route to fly, and assembling cockpit and cabin crews to staff it. Any one of these tasks poses a challenge, but integrating them into a monthly, unified system can frustrate the best of planners.听

In 2017, assistant professor of decision sciences Melda听Ormeci听Matoglu听and her co-authors rocked the data analytics world with their solution to the unified flight system problem.听OrmeciMatoglu鈥檚听study measured countless permutations and, accounting for all complex aviation rules 鈥� ranging from minimum connection times to maximum landings a crewmember can do in a day, to rules governing location and flight patterns 鈥� and arrived at a formula for getting planes and crew in the air, keeping them there, and saving money.听听

Since then,听Ormeci听Matoglu听has turned her eyes from the friendly skies toward the more earthy matters of how companies can manage capacity for minimal cost.听听

鈥淢any industries face the problem of managing capacity in the face of unpredictably varying demand,鈥� she says. 鈥淔or example, adjusting the number of manufacturing lines to meet outstanding orders, determining the staffing levels at a call center, or deploying webservers to handle internet traffic.鈥澨�

The name of the game here is 鈥渄rift control,鈥� which refers to the difference between rate of service and demand.听

鈥淚n the airline studies, I looked for good solutions. In the drift control studies, I鈥檓 looking for optimal solutions to minimize the long-run average cost of adding capacity versus losing customers who tire of waiting in line, being on hold, or queuing up for server space,鈥� she says.听

Rather than reactively hiring and firing staff or investing heavily in more equipment or technology every time demand spikes or dips, managers can use听Ormeci听Matoglu鈥檚听formula to automatically switch to higher or lower production rates to meet fluctuation in demand.听听

Best of all, she says, 鈥淥ur solution removes the guesswork in terms of the optimal way to manage job lines or interactions, so now managers can use our results, regardless of what industry they鈥檙e in.鈥澨�

RECENT SELEC TED PUBLIC ATIONS听

Ormeci听Matoglu, M.,听Vande听Vate, J., &Yu, H. (2019). The economic average cost Brownian control problem.听Advances in Applied Probability, 51.听

Ozener, O.,听Ormeci听Matoglu, M., & Erdogan, G. et al. (2017). Solving a large-scale integrated fleet assignment and crew pairing problem.听Annals of Operations Research, 253.听听

Ormeci听Matoglu, M.,听Vande听Vate, J., & Wong, H. (2015). Solving the drift control problem.Stochastic Systems, 5.听

Erdogan, G.,听Haouari, M.,听Ormeci听Matoglu, M., &听Ozener, O. (2015). Solving a large-scale crew pairing problem.听Journal of Operational Research Society, 66.听