Effect of Uncertainty on Deterministic Runway Scheduling

Abstract

Active runway scheduling involves scheduling departures for takeoffs and arrivals for runway crossing subject to numerous constraints. This paper evaluates the effect of uncertainty on a deterministic runway scheduler. The evaluation is done against a firstcome-first-serve scheme. In particular, the sequence from a deterministic scheduler is frozen and the times adjusted to satisfy all separation criteria; this approach is tested against FCFS. The comparison is done for both system performance (throughput and system delay) and predictability, and varying levels of congestion are considered. The modeling of uncertainty is done in two ways: as equal uncertainty in availability at the runway as for all aircraft, and as increasing uncertainty for later aircraft. Results indicate that the deterministic approach consistently performs better than first-come-first-serve in both system performance and predictability.

Towards a Fast-time Simulation Analysis of Benefits of the Spot and Runway Departure Advisor

“…The average delay of the delay-optimal simulation was not less than that of the first-come-first-served simulation. This is in contrast to stand-alone analysis of delay-optimal schedulers, [6][7][8][9][10][11][12] which showed that they reduce delays and increase throughput relative to simple algorithmic models of human controllers.…”

“…The GRP simulation did not measurably increase the throughput rate of runway 17R or decrease average departure delay despite other research [6][7][8][9][10][11][12] that shows in standalone analysis that optimal departure scheduling can decrease delay. Future research could investigate other traffic scenarios, including runway crossings in the optimal scheduling process, and better prediction and control methods to see if optimal scheduling can increase the runway throughput.…”

“…An algorithm for generating minimum-delay and maximum-throughput departure sequences has been developed using the basic principles of mixed integer linear programming [6][7][8][9][10] and of dynamic programming. 11 The dynamic programming version produced in less execution time similar results to the mixed integer linear programming version.…”

Optimization of Aircraft Pushback Slot Allocation under Multihotspot Restriction

“…Airport surface scheduling optimization has been an active field for research. Ioannis Simaiakis et al developed a queuing model of the departure process in order to describe quantitatively how queues form on the surface and what factors lead to the increased taxi-out times [1].Andrea D' Ariano et al studied the problem of sequencing aircraft take-off and landing operations at congested airports, but the real time of the solution was not good [2].Ying Dong et al proposed that the airport ground system can be regarded as an airport network topology and established an optimization model [3].Gautam Gupta et al evaluated the effect of uncertainty on a deterministic runway scheduler [4].Atkin et al used a model to predict the delays at the stands or the runway in order to absorb this time at the stand, but they did not analyze the delays at hotspot [5]. Christofas Stergianos et al investigated the importance of the pushback process in the routing and scheduling problem [6].…”

Predictability in Airport Surface Operation Management

“…In a trajectory-based air traffic management system, investigation of predictability on the surface is not just beneficial to the airport operations but could also improve performance in the downstream. However, in the context of airport surface operation management, performance assessment has mainly been conducted with respect to delay [1][2][3][4] , capacity [5][6][7][8] and efficiency 3,9,10 . Little work has been done to measure predictability, even though various stakeholders recognize the importance of predictability 11,12 .…”