Optimization of Integrated Departures and Arrivals 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 post-analysis process of deterministic optimization is one way to learn the impact of uncertainty and to avoid unexpected outcomes. This work presents a 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 cost besides total delay. Costs under uncertainty were evaluated using Monte Carlo simulations. The Pareto fronts that contain a set of solutions were identified and the trade-off 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 can identify compromise solutions and help decision-makers reduce controller intervention while achieving low delays.

A Stochastic Scheduler for Integrated Arrival, Departure, and Surface Operations in Los Angeles

“…The interactions between Fillmore arrivals and Northbound departures in Los Angeles terminal airspace ( Figure 1) have been investigated in previous works 13,16,17 on scheduling with competing resources. This work extends the scope to the entire LAX airspace by including all arrivals, departures and surface operations.…”

“…The stochastic scheduler combines NSGA and Monte Carlo simulation. 13,16,17,28 The decision variables including speeds, routes, delays, and runway assignment are coded as "genes", and each solution with a set of decision variables is marked as an "individual". In NSGA, a population with hundreds of "individuals" evolves at each generation in terms of their costs through operations of "crossover", "mutation", "ranking", and "selection".…”

“…15 For scheduling integrated operations using both route and speed controls, a stochastic scheduler based on Non-dominated Sorting Genetic Algorithm (NSGA) and Monte Carlo simulations was proposed to identify optimal and robust schedules. 16,17 The optimization algorithm in the scheduler takes uncertainty into account by calculating costs stochastically over thousands of possible estimated arrival/departure times that follow gaussian distributions. Another stochastic scheduler 18 that combines job-shop scheduling method and SAA was also proposed for solving integrated scheduling problems.…”

“…A more integrated scheduler that coordinates arrivals, departures, and surface operations is necessary to provide more efficiency and/or even consider user preference by removing barriers between different operations. 19 This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations on the basis of previous works 16,17 using NSGA and Monte Carlo simulations. It advances the sequential and stochastic scheduler developed previously and extends its application to arrivals, departures, and surface operations in the entire terminal area.…”

Dynamic stochastic scheduler for integrated arrivals and departures

“…A conventional way to deal with uncertainty is to use extra buffers in addition to the required aircraft separations. Previous work [14] proposed a proactive method -a stochastic scheduler -that directly takes uncertainty into account by optimizing integrated arrivals and departures under uncertainty.…”