Designing airline networks faster using convex optimization for cost and passenger efficiency

What is it about?

Air transportation relies on complex networks that connect airports. Designing these networks is difficult because airlines must balance costs, passenger demand, and competition. Traditional methods to design networks are very detailed but too slow for large systems. In this study, we introduce a new mathematical approach based on convex optimization. It lets us design airline networks for larger and more realistic case studies, and also faster to compute. We show that our method finds practical solutions for U.S. domestic air travel, helping airlines plan hubs and routes efficiently while considering passenger choices and costs.

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Photo by Chris Leipelt on Unsplash

Photo by Chris Leipelt on Unsplash

Why is it important?

What is unique about our work is that we replace traditional methods to solve mixed integer programs (MIP) with convex optimization, which is both faster and more scalable. This allows us to design airline networks that include many more airports and routes than was possible before. Our approach is also timely, as air travel faces increasing competition and demand for efficiency. By making network planning more practical, our method can help airlines and regulators test different scenarios quickly, improve service, and lower costs. This difference could shape how future airline networks are planned worldwide.

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