A robust optimization approach for designing multi-period airport hub network with uncertain capacity

Abstract

Airports are increasing their capacity to accelerate and facilitate travel and cargo delivery. At the same time, they aim to decrease expenses on delays caused by capacity overflow, encouraging policymakers to plan to enhance the capacity of crowded airports for the long term and set their transportation policies accordingly. This study develops a mathematical model for designing a network of airports with hub location problems (HLPs) with uncertain practical capacity in addition to their deterministic nominal capacity, using mixed integer programming (MIP). Our methodology proposes a robust optimization framework for uncertain capacity in hub airport facilities. Also, we utilize a practical approach for calculating the transit flow in hub airports by decomposing the flow into incoming, transiting, and outgoing statuses. We use a tailored Benders decomposition algorithm (BDA) to facilitate the solution effort. Numerical results using data envelopment analysis (DEA) show a notable increase in the efficiency of the hub airports with the proposed method. Finally, airport managers can plan to improve the need for air transport infrastructure over a long period.