Scheduling aircraft landings - the static case

This is the publisher version of the article, obtained from the link below.In this paper, we consider the problem of scheduling aircraft (plane) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and that separation criteria between the landing of a plane and the landing of all successive planes are respected. We present a mixed-integer zero–one formulation of the problem for the single runway case and extend it to the multiple runway case. We strengthen the linear programming relaxations of these formulations by introducing additional constraints. Throughout, we discuss how our formulations can be used to model a number of issues (choice of objective function, precedence restrictions, restricting the number of landings in a given time period, runway workload balancing) commonly encountered in practice. The problem is solved optimally using linear programming-based tree search. We also present an effective heuristic algorithm for the problem. Computational results for both the heuristic and the optimal algorithm are presented for a number of test problems involving up to 50 planes and four runways.J.E.Beasley. would like to acknowledge the financial support of the Commonwealth Scientific and Industrial Research Organization, Australia

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

Runway Operations Management: Models, Enhancements, and Decomposition Techniques

Air traffic loads have been on the rise over the last several decades and are expected to double, and possibly triple in some regions, over the coming decade. With the advent of larger aircraft and ever-increasing air traffic loads, aviation authorities are continually pressured to examine capacity expansions and to adopt better strategies for capacity utilization. However, this growth in air traffic volumes has not been accompanied by adequate capacity expansions in the air transport infrastructure. It is, therefore, predicted that flight delays costing multi-billion dollars will continue to negatively impact airline companies and consumers. In airport operations management, runways constitute a scarce resource and a key bottleneck that impacts system-wide capacity (Idris et al. 1999). Throughout the three essays that form this dissertation, enhanced optimization models and effective decomposition techniques are proposed for runway operations management, while taking into consideration safety and practical constraints that govern access to runways. Essay One proposes a three-faceted approach for runway capacity management, based on the runway configuration, a chosen aircraft assignment/sequencing policy, and an aircraft separation standard as typically enforced by aviation authorities. With the objective of minimizing a fuel burn cost function, we propose optimization-based heuristics that are grounded in a classical mixed-integer programming formulation. By slightly altering the FCFS sequence, the proposed optimization-based heuristics not only preserve fairness among aircraft, but also consistently produce excellent (optimal or near optimal) solutions. Using real data and alternative runway settings, our computational study examines the transition from the (Old) Doha International Airport to the New Doha International Airport in light of our proposed optimization methodology. Essay Two examines aircraft sequencing problems over multiple runways under mixed mode operations. To curtail the computational effort associated with classical mixed-integer formulations for aircraft sequencing problems, valid inequalities, pre-processing routines and symmetry-defeating hierarchical constraints are proposed. These enhancements yield computational savings over a base mixed-integer formulation when solved via branch-and-bound/cut techniques that are embedded in commercial optimization solvers such as CPLEX. To further enhance its computational tractability, the problem is alternatively reformulated as a set partitioning model (with a convexity constraint) that prompts the development of a specialized column generation approach. The latter is accelerated by incorporating several algorithmic features, including an interior point dual stabilization scheme (Rousseau et al. 2007), a complementary column generation routine (Ghoniem and Sherali, 2009), and a dynamic lower bounding feature. Empirical results using a set of computationally challenging simulated instances demonstrate the effectiveness and the relative merits of the strengthened mixed-integer formulation and the accelerated column generation approach. Essay Three presents an effective dynamic programming algorithm for solving Elementary Shortest Path Problems with Resource Constraints (ESPPRC). This is particularly beneficial, because the ESPPRC structure arises in the column generation pricing sub-problem which, in turn, causes computational challenges as noted in Essay Two. Extending the work by Feillet et al. (2004), the proposed algorithm dynamically constructs optimal aircraft schedules based on the shortest path between operations while enforcing time-window restrictions and consecutive as well as nonconsecutive minimum separation times between aircraft. Using the aircraft separation standard by the Federal Aviation Administration (FAA), our computational study reports very promising results, whereby the proposed dynamic programming approach greatly outperforms the solution of the sub-problem as a mixed-integer programming formulation using commercial solvers such as CPLEX and paves the way for developing effective branch-and-price algorithms for multiple-runway aircraft sequencing problems

An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc

A Concept of Operations for Far-Term Surface Trajectory-Based Operations (STBO)

The goal of this far-term STBO (Surface Trajectory-Based Operations) ConOps (Concept of Operations) is to increase the efficiency and predictability of airport surface operations, and reduce the environmental impact, by incorporating a time-based component to surface operations. In the far-term NextGen timeframe, airport surface operations will transition from current-day first-come, first-served operations, to strategically scheduled operations in which pilots are recruited as active participants in meeting the precise time-based goals of NextGen surface operations. The far-term STBO concept includes two-phases. Phase 1 introduces time-based traffic flow constraint points, which divide the taxi route into segments with an assigned Required Time of Arrival (RTA). This Phase 1 approach provides temporal certainty only near the traffic flow constraint points, but not in between. Minimal augmentations to the flight deck are required to support required time of arrival (RTA) management. Phase 2 further increases precision and efficiency by introducing full four-dimensional (4D) trajectories, with an x-y location for all times t. This phase assumes adoption of advanced flight deck equipage enabling higher temporal precision sufficient to support aircraft conformance to 4D trajectories. This allows more precision and less temporal uncertainty at all times along the route

Scheduling Aircraft Landings under Constrained Position Shifting

Optimal scheduling of airport runway operations can play an important role in improving the safety and efficiency of the National Airspace System (NAS). Methods that compute the optimal landing sequence and landing times of aircraft must accommodate practical issues that affect the implementation of the schedule. One such practical consideration, known as Constrained Position Shifting (CPS), is the restriction that each aircraft must land within a pre-specified number of positions of its place in the First-Come-First-Served (FCFS) sequence. We consider the problem of scheduling landings of aircraft in a CPS environment in order to maximize runway throughput (minimize the completion time of the landing sequence), subject to operational constraints such as FAA-specified minimum inter-arrival spacing restrictions, precedence relationships among aircraft that arise either from airline preferences or air traffic control procedures that prevent overtaking, and time windows (representing possible control actions) during which each aircraft landing can occur. We present a Dynamic Programming-based approach that scales linearly in the number of aircraft, and describe our computational experience with a prototype implementation on realistic data for Denver International Airport

Simulation evaluation of TIMER, a time-based, terminal air traffic, flow-management concept

A description of a time-based, extended terminal area ATC concept called Traffic Intelligence for the Management of Efficient Runway scheduling (TIMER) and the results of a fast-time evaluation are presented. The TIMER concept is intended to bridge the gap between today's ATC system and a future automated time-based ATC system. The TIMER concept integrates en route metering, fuel-efficient cruise and profile descents, terminal time-based sequencing and spacing together with computer-generated controller aids, to improve delivery precision for fuller use of runway capacity. Simulation results identify and show the effects and interactions of such key variables as horizon of control location, delivery time error at both the metering fix and runway threshold, aircraft separation requirements, delay discounting, wind, aircraft heading and speed errors, and knowledge of final approach speed

The trade-off between taxi time and fuel consumption in airport ground movement

Environmental impact is a very important agenda item in many sectors nowadays, which the air transportation sector is also trying to reduce as much as possible. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and it is still unknown whether fuel burn and environmental impact reductions will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used

Planning and reconfigurable control of a fleet of unmanned vehicles for taxi operations in airport environment

The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one