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The article focuses on air transportation industry operates under pervasive long- and short-term uncertainty. Topics inlcude the U.S. Department of Transportation's Bureau of Transportation Statistics has reported in 2019, the passenger flights in the U.S. has delayed by more than fifteen minutes were either cancelled or diverted, and growing interest in explicitly considering uncertainty when developing planning for air transport operations has reflected in the academic literature.

This paper presents a new integer programming (IP) model for large-scale instances of the air traffic flow management (ATFM) problem. The model covers all the phases of each flight--i.e., takeoff, en route cruising, and landing--and solves for an optimal combination of flow management actions, including ground-holding, rerouting, speed control, and airborne holding on a flight-by-flight basis. A distinguishing feature of the model is that it allows for rerouting decisions. This is achieved through the imposition of sets of "local" conditions that make it possible to represent rerouting options in a compact way by only introducing some new constraints. Moreover, three classes of valid inequalities are incorporated into the model to strengthen the polyhedral structure of the underlying relaxation. Computational times are short and reasonable for practical application on problem instances of size comparable to that of the entire U.S. air traffic management system. Thus, the proposed model has the potential of serving as the main engine for the preliminary identification, on a daily basis, of promising air traffic flow management interventions on a national scale in the United States or on a continental scale in Europe. [ABSTRACT FROM AUTHOR]