Lui, Go Nam and Lulli, Guglielmo (2025) 3D Optimal Airspace Sector Design with Mixed Integer Linear Programming. In: INFORMS Annual Meeting 2025, 2025-10-26, Atlanta.
Full text not available from this repository.Abstract
The increasing complexity of air traffic management and the growing demand for efficient airspace utilization have driven the development of smart upper airspace systems that leverage advanced operations research methodologies. This presentation present the application of operations research techniques in the design and configuration of intelligent upper airspace frameworks in the SMARTS project, focusing on optimization models that enhance traffic flow management and improve overall system performance. We explore two key applications: First, the optimal design of airspace sectors. Mathematical programming models, such as Mixed Integer Programming (MIP), are employed to create sectors that balance controller workload and ensure smooth traffic flow, with the connectivity and traffic flow convexity consideration. Complementary heuristic approaches demonstrate the ability to achieve near-optimal solutions rapidly, crucial for operational agility. Second, dynamic airspace configuration. Here, OR techniques, including integer linear programming and graph-based algorithms, enable the selection of optimal airspace configurations in response to fluctuating traffic demand and uncertainty. These methods aim to maximize capacity, minimize delays, and provide robust plans adaptable to unforeseen demand surges. Validated with real-world data, these OR applications provide theoretically sound and practically implementable tools. They offer significant improvements in ATM efficiency, workload distribution, and system robustness, paving the way for more adaptive and resilient airspace management in the face of evolving challenges.