Integrated Arrival- and Departure-Schedule Optimization Under Uncertainty

Abstract

In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a postanalysis process of deterministic optimization and imposing a safety buffer in deterministic optimization are two ways to learn and alleviate the impact of uncertainty and to avoid unexpected outcomes. This work presents a third and direct way to take uncertainty into consideration during the optimization. The impact of uncertainty was incorporated into cost evaluations when searching for the optimal solutions. The controller intervention count was computed using a heuristic model and served as another stochastic cost in addition to total delay. Costs under uncertainty were evaluated using Monte Carlo simulations. The Pareto fronts that contained a set of solutions were identified, and the tradeoff between delays and controller intervention count was shown. Solutions that shared similar delays but had different intervention counts were investigated. The results showed that optimization under uncertainty could identify compromise solutions on Pareto fonts, which is better than deterministic optimization with extra safety buffers. It helps decision makers reduce controller intervention while achieving low delays.