Large-scale, stochastic, and data-driven optimization for transportation systems

This presentation includes two papers. The first one proposes an Integrated Model of Scheduling and Operations in Airport Networks to jointly optimize scheduling interventions and ground-holding operations across airport networks, under operating uncertainty. It is formulated as a two-stage stochastic program with integer recourse. To solve it, we develop an original decomposition algorithm with provable solution quality guarantees. The algorithm relies on new optimality cuts—dual integer cuts—which leverage the reduced costs of the dual linear programming relaxation of the second-stage problem. We use a scenario generation approach to construct representative scenarios from historical records of operations. Computational experiments show that our algorithm yields near-optimal solutions for the entire US National Airspace System network. Ultimately, the proposed approach enhances airport demand management models through scale integration (by capturing network-wide interdependencies) and scope integration (by capturing interdependencies between scheduling and operations).