Optimizing integrated terminal airspace operations under uncertainty

Abstract

In the terminal airspace, integrated departures and arrivals have the potential to increase operations efficiency. Recent research has developed geneticalgorithm-based schedulers for integrated arrival and departure operations under uncertainty. This paper presents an alternate method using a machine jobshop scheduling formulation to model the integrated airspace operations. A multistage stochastic programming approach is chosen to formulate the problem and candidate solutions are obtained by solving sample average approximation problems with finite sample size. Because approximate solutions are computed, the proposed algorithm incorporates the computation of statistical bounds to estimate the optimality of the candidate solutions. A proof-ofconcept study is conducted on a baseline implementation of a simple problem considering a fleet mix of 14 aircraft evolving in a model of the Los Angeles terminal airspace. A more thorough statistical analysis is also performed to evaluate the impact of the number of scenarios considered in the sampled problem. To handle extensive sampling computations, a multithreading technique is introduced.

A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty

“…Typical approaches used for uncertainty optimization include stochastic programming, robust optimization, and chance-constrained optimization [22,23]. The most common applications of stochastic programming are the two-stage or multi-stage stochastic problems [24,25], where one assumes that the uncertain parameters can be represented by the known probability distribution based on complete knowledge of the uncertainty. The selection of the tackled problems for the stages are usually based on the prioritization of the particular information required.…”

A framework for integrating arrival, departure, and surface operations scheduling

“…In addition to improving departure trajectory prediction with CDM gate pushback times and RNAV departure routing, arrival schedulers may use stochastic techniques to mitigate the uncertainty. Stochastic scheduling incorporates flight arrival-time uncertainty at the coordination point by including the probability of separation in the cost function [49][50][51][52]. If the flight scheduler cannot get an accurate 40minute advance gate pushback schedule from surface CDM, some system benefits may still be possible using flight plan departure times, even though surface scheduler assigned departure times would be much better.…”

A 0–1 mixed‐integer program‐based group‐and‐release strategy for solving the integrated runway scheduling and taxiway routing problem

“…The performance of these two approaches was analyzed using data from Detroit (DTW) airport, and they concluded that, during peak times, the integrated approach provided an improved schedule, albeit at the cost of computational performance. Bosson et al (2015) generated a set of predefined paths for each aircraft, and developed an algorithm to compute optimal aircraft schedules and routes for 13 aircraft (using data from LAX airport). The major drawback of their model is that taxiway conflicts are not explicitly taken into consideration.…”