A Study of Tradeoffs in Scheduling Terminal-Area Operations

Abstract

| The terminal area surrounding an airport is an important component of the air transportation system, and efficient terminal-area schedules are essential for accommodating the projected increase in air traffic demand. Aircraft arrival schedules are subject to a variety of operational constraints, such as minimum separation for safety, required arrival time-windows, limited deviation from a first-come firstserved sequence, and precedence constraints. There is also a range of objectives associated with multiple stakeholders that could be optimized in these schedules; the associated tradeoffs are evaluated in this paper. A dynamic programming algorithm for determining the minimum cost arrival schedule, given aircraft-dependent delay costs, is presented. The proposed approach makes it possible to determine various tradeoffs in terminal-area operations. A comparison of maximum throughput and minimum average delay schedules shows that the benefit from maximizing throughput could be at the expense of an increase in average delay, and that minimizing delay is the more advantageous of the two objectives in most cases. A comprehensive analysis of the tradeoffs between throughput and fuel costs and throughput and operating costs is conducted, accounting for both the cost of delay (as reported by the airlines) and the cost of speeding up when possible (from models of aircraft performance).

Extended Aircraft Arrival Management Under Uncertainty: A Computational Study

“…The case in which predicted operations times are known with certainty, called the deterministic case, has been thoroughly studied in the literature [4,7,8], while the case under uncertainty has less often been addressed. So far in the related literature, three main approaches to optimization under uncertainty were applied to ASSP: probabilistic [9,10], stochastic [11][12][13] and robust [14][15][16] approaches. Pioneer studies such as [9,10] mainly enriched deterministic models by probability constraints and/or by a probability objective-value function.…”

“…So far in the related literature, three main approaches to optimization under uncertainty were applied to ASSP: probabilistic [9,10], stochastic [11][12][13] and robust [14][15][16] approaches. Pioneer studies such as [9,10] mainly enriched deterministic models by probability constraints and/or by a probability objective-value function. Stochastic programming models, including two-stage and multi-stage models, were proposed in [11][12][13].…”

Flight behavior recognizing in terminal area based on support vector machine

“…It is an important component of the air transportation system, and efficient terminal-area schedules are essential for accommodating the projected increase in air traffic demand [6]. Terminal area describes a volume of controlled airspace set up at the confluence of airways in the vicinity of one or more major airports to protect traffic climbing out from and descending into the airports.…”

“…Currently, most of the ATC functions in terminal regions are executed by humans, making the process inefficient and inflexible. Therefore, automated ATC has received considerable attention in the past few years [6] [7].…”