Conference Paper

Upper and Lower Bound Estimation of Runway Throughput in the Presence of Uncertainty

To read the full-text of this research, you can request a copy directly from the authors.


Upper and lower bound estimation for runway capacity are presented in the presence of various uncertainties for a single runway airport. Currently, arrivals touch down on runways are either assumed to be predetermined and controllable, or fully controllable. Also, uncertainties in aircraft trajectories, whether they are on the ground or in the air, are dealt with reactively as opposed to proactively. As one of the solutions to these problems stated, a two stage stochastic model can be formulated to determine feasible and optimal schedule for both arrival and departure at runway, taxiway and ramp. In order to show the necessity of developing the stochastic model for optimizing arrival and departure schedule in feasible bound, several study cases are introduced along with the results using Monte-Carlo simulation for a single runway airport in this paper. In this study, both arrival and departure capacity are calculated for various service rates of runway for 1) predetermined/uncontrollable and 2) fully controllable arrival stream. Also, the case with and without uncertainty from arrival-departure interaction and taxi time distribution are included in Monte-Carlo simulation to study the effect of uncertainty. Although both arrival and departure capacity decrease with number of uncertainty added in the simulation, the average departure capacity increases by modifying the arrival stream from an non-integer to an integer problem while maintaining similar average arrival capacity, especially at busy hour of airport. This result shows the benefit of modifying the arrival stream as an integer problem for optimizing the runway resource as well as overall airport operation efficiency. Based on numerical results, predetermined/uncontrollable and controllable arrival stream are planned to be used for lower and upper bound estimation, respectively, in the presence of various uncertainty in two stage stochastic model in the future.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Full-text available
This paper addresses the challenge of building an automated decision support methodology to tackle the complex problem faced every day by runway controllers at London Heathrow Airport. Aircraft taxi from stands to holding areas at the end of the take-off runway where they wait in queues for permission to take off. A runway controller attempts to find the best order for aircraft to take off. Sequence-dependent separation rules that depend upon aircraft size, departure route and speed group ensure that this is not a simple problem to solve. Take-off time slots on some aircraft and the need to avoid excessive delay for any aircraft make this an even more complicated problem. Making this decision at the holding area helps to avoid the problems of unpredictable push-back and taxi times, but introduces a number of complex spatial constraints that would not otherwise exist. The holding area allows some flexibility for interchange of aircraft between queues, but this is limited by its physical layout. These physical constraints are not usually included in academic models of the departure problem. However, any decision support system to support the take-off runway controller must include them. We show, in this paper, that a decision support system could help the controllers to significantly improve the departure sequence at busy times of the day, by considering the taxiing aircraft in addition to those already at the holding area. However, undertaking this re-introduces the issue of taxi time uncertainty, the effect of which we explicitly measure in these experiments. Empirical results are presented for experiments using real data from different times of the day, showing how the performance of the system varies depending upon the volume of traffic and the accuracy of the provided taxi time estimations. We conclude that the development of a good taxi time prediction system is key to maximising the benefits, although benefits can be observed even without this.
In this paper a combinatorial optimization approach to aircraft sequencing problem is proposed. In particular the single runway case, with the hypotesis that airplanes wait to land at different times, is considered. It is shown that the problem of maximizing the runway utilization can be modeled as a n job-one machine scheduling problem with non zero ready times, sequence dependent processing times, and with the objective of minimizing the maximum completion time. A solution algorithm is outlined and tested by various examples and the computational results are discussed. Implementation issues are also considered and suggestions on how improve the algorithm performances are made.
Aircraft arrive in a random fashion into a terminal area seeking to land at a given runway. The aircraft are differentiated by their landing velocities. All aircraft are required to maintain a prespecified minimum horizontal separation distance and also fly on a common final approach. As a consequence, the minimum interarrival time separation is interactive, i.e., a function of the landing velocities of the preceding and following aircraft as well as the separation minimum and final approach length. The controller's decision-making problem in sequencing the aircraft, termed dynamic scheduling, is formulated in this dynamic environment. It is observed that the first-come, first-serve discipline is inefficient and the system properties employing optimality objectives of maximum throughput and minimum delay are investigated. The solutions must be updated with each new arrival and, as a result, the solutions employing these optimality objectives are shown to have undesirable properties, including 1) a priority structure with the potential for indefinite delay; 2) non-implementable updating assignments; 3) computationally intractable solutions in real time. As a consequence of this analysis, a decision methodology termed Constrained Position Shifting (CPS) is proposed to eliminate these undesirable properties. CPS prohibits an aircraft from being shifted more than a given number of positions from its first-come, first-serve position. The CPS methodology is then shown via simulation to be practical, efficient and extremely flexible, with the following properties: 1. increases the runway throughput rate; 2. treats individual aircraft equitably; 3. treats aircraft velocity classes equitably; 4. particularly successful during peak periods; 5. well within the capabilities of today's computers. The simulation is designed to compare identical arrival streams under various strategies. The simulation-aided analysis is then extended to include "heavy" jets (with aircraft dependent separation minima) and also mixed operations (arrivals and departures). Even greater improvements in terminal area levels of service are demonstrated for these extensions.
Models and algorithms for real-time control of the terminal area are proposed. We consider two cases: in the first one (static) we assume that there is a set of aircraft to be sequenced for which we know in advance their entry time in the terminal area; in the second one (dynamic), the entry times of future aircraft are unknown and the sequence of aircraft is recomputed whenever a new aircraft approaches the terminal area. For the static case, we model the sequencing problem as a cumulative traveling salesman problem with ready times and propose two lower bounds for testing heuristic solutions. For the dynamic case, where only a limited knowledge of the arrivals is assumed, we add to the basic model a set of constraints which allow the controller to maintain given patterns of the landing sequences previously generated. For both cases, heuristic algorithms are proposed and computational results are discussed.
Conference Paper
Reducing the delays of the departing aircraft can potentially lead to improving the efficiency of the surface operations at airports. This paper addresses a departure scheduling problem with an objective to reduce total aircraft delays subject to timing and ordering constraints. The ordering constraints model the queuing area of airports where the aircraft align themselves in the form of chains before departing. By exploiting the structure of the problem, a generalized dynamic programming approach is presented to solve the departure scheduling problem optimally. Computational results indicate that the approach presented in this paper is reasonably fast, i:e:, it takes less than one tenth of a second on average to solve a 40 aircraft problem. Also, the approach produces optimal sequences whose delay is approximately 12 minutes, on average less than the delays produced by the First Come First Serve (FCFS) sequences. Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Aircraft taxiing on the surface contribute significantly to the fuel burn and emissions at airports. This paper investigates the possibility of reducing fuel burn and emissions from surface operations through a reduction of the taxi times of departing aircraft. A novel approach is proposed that models the aircraft departure process as a queuing system, and attempts to reduce taxi times and emissions through improved queue management strategies. The departure taxi (taxi-out) time of an aircraft is represented as a sum of three com- ponents, namely, the unimpeded taxi-out time, the time spent in the departure queue, and the congestion delay due to ramp and taxiway interactions. The dependence of the taxi-out time on these factors is analyzed and modeled. The performance of the model is validated through a comparison of its predictions with observed data at Boston's Logan International Airport (BOS). The reductions in taxi-out times from the proposed queue management strategy are translated to reductions in fuel burn and emissions using ICAO engine models for the taxi phase of the flight profile.
Personal Correspondence
  • G Chesterton