Airport Runway Slots: Limits to Growth

The United States–European Union market accounts for approximately 25% of all international tourist arrivals worldwide, and is arguably the busiest market in the world. This paper argues that landing slot policy and the manner in which airport capacity is allocated among airlines across the north Atlantic is likely to underpin the future geographic structure of the tourism industry. By analyzing the historical evolution of slot policy, this paper attempts to enhance the extant literature on how government authorities allocate scarce airport resources. The paper concludes by arguing that various slot reform proposals need to be adopted to make airports more “elastic” when managing origin-destination tourist flows

Exact and Heuristic Algorithms for Runway Scheduling

This paper explores the Single Runway Scheduling (SRS) problem with arrivals, departures, and crossing aircraft on the airport surface. Constraints for wake vortex separations, departure area navigation separations and departure time window restrictions are explicitly considered. The main objective of this research is to develop exact and heuristic based algorithms that can be used in real-time decision support tools for Air Traffic Control Tower (ATCT) controllers. The paper provides a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the SRS problem, but may prove unusable for application in real-time environment due to large computation times for moderate sized problems. We next propose a second algorithm that uses heuristics to restrict the search space for the DP based algorithm. A third algorithm based on a combination of insertion and local search (ILS) heuristics is then presented. Simulation conducted for the east side of Dallas/Fort Worth International Airport allows comparison of the three proposed algorithms and indicates that the ILS algorithm performs favorably in its ability to find efficient solutions and its computation times

On-line decision support for take-off runaway scheduling at London Heathrow Airport

The research problem considered in this thesis was presented by NATS, who are responsible for the take-off runway scheduling at London Heathrow airport. The sequence in which aircraft take off is very important and can have a huge effect upon the throughput of the runway and the consequent delay for aircraft awaiting take-off. Sequence-dependent separations apply between aircraft at take-off, some aircraft have time-slots within which they must take-off and all re-sequencing performed by the runway controller has to take place within restrictive areas of the airport surface called holding areas. Despite the complexity of the task and the short decision time available, take-off sequencing is performed manually by runway controllers. In such a rapidly changing environment, with much communication and observation demanded of the busy controller, it is hardly surprising that sub-optimal mental heuristics are currently used. The task presented by NATS was to develop the decision-making algorithms for a decision support tool to aid a runway controller to solve this complex real-world problem. A design for such a system is presented in this thesis. Although the decision support system presents only a take-off sequence to controllers, it is vitally important that the movement within the holding area that is required in order to achieve the re-sequencing is both easy to identify and acceptable to controllers. A key objective of the selected design is to ensure that this will always be the case. Both regulatory information and details of controller working methods and preferences were utilised to ensure that the presented sequences will not only be achievable but will also be acceptable to controllers. A simulation was developed to test the system and permit an evaluation of the potential benefits. Experiments showed that the decision support system found take-off sequences which significantly reduced the delay compared with those that the runway controllers actually used. These sequences had an equity of delay comparable with that in the sequences the controllers generated, and were achieved in a very similar way. Much of the benefit that was gained was a result of the decision support system having visibility of the taxiing aircraft in addition to those already queueing for the runway. The effects of uncertainty in taxi times and differing planning horizons are explicitly considered in this thesis. The limited decision time available ensures that it is not practical for a runway controller to consider as many aircraft as the decision support algorithms can. The results presented in this thesis indicate that huge benefits may be possible from the development of a system to simplify the sequencing task for the controllers while simultaneously giving them greater visibility of taxiing aircraft. Even beyond these benefits, however, the system described here will also be seen to have further potential benefits, such as for evaluating the effects of constraints upon the departure system or the flexibility of holding area structures

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

A Study of Heuristic Approaches for Runway Scheduling for the Dallas-Fort Worth Airport

Recent work in air transit efficiency has increased en-route efficiency to a point that airport efficiency is the bottleneck. With the expected expansion of air transit it will become important to get the most out of airport capacity. Departure scheduling is an area where efficiency stands to be improved, but due to the complicated nature of the problem an optimal solution is not always forthcoming. A heuristic approach can be used to find a sub-optimal take-off order in a significantly faster time than the optimal solution can be found using known methods. The aim of this research is to explore such heuristics and catalog their solution characteristics. A greedy approach as well as a k-interchange approach were developed to find improved takeoff sequences. When possible, the optimal solution was found to benchmark the performance of the heuristics, in general the heuristic solutions were within 10-15% of the optimal solution. The heuristic solutions showed improvements of up to 15% over the first-in first-out order with a running time around 4 ms

On-line decision support for take-off runaway scheduling at London Heathrow Airport

The research problem considered in this thesis was presented by NATS, who are responsible for the take-off runway scheduling at London Heathrow airport. The sequence in which aircraft take off is very important and can have a huge effect upon the throughput of the runway and the consequent delay for aircraft awaiting take-off. Sequence-dependent separations apply between aircraft at take-off, some aircraft have time-slots within which they must take-off and all re-sequencing performed by the runway controller has to take place within restrictive areas of the airport surface called holding areas. Despite the complexity of the task and the short decision time available, take-off sequencing is performed manually by runway controllers. In such a rapidly changing environment, with much communication and observation demanded of the busy controller, it is hardly surprising that sub-optimal mental heuristics are currently used. The task presented by NATS was to develop the decision-making algorithms for a decision support tool to aid a runway controller to solve this complex real-world problem. A design for such a system is presented in this thesis. Although the decision support system presents only a take-off sequence to controllers, it is vitally important that the movement within the holding area that is required in order to achieve the re-sequencing is both easy to identify and acceptable to controllers. A key objective of the selected design is to ensure that this will always be the case. Both regulatory information and details of controller working methods and preferences were utilised to ensure that the presented sequences will not only be achievable but will also be acceptable to controllers. A simulation was developed to test the system and permit an evaluation of the potential benefits. Experiments showed that the decision support system found take-off sequences which significantly reduced the delay compared with those that the runway controllers actually used. These sequences had an equity of delay comparable with that in the sequences the controllers generated, and were achieved in a very similar way. Much of the benefit that was gained was a result of the decision support system having visibility of the taxiing aircraft in addition to those already queueing for the runway. The effects of uncertainty in taxi times and differing planning horizons are explicitly considered in this thesis. The limited decision time available ensures that it is not practical for a runway controller to consider as many aircraft as the decision support algorithms can. The results presented in this thesis indicate that huge benefits may be possible from the development of a system to simplify the sequencing task for the controllers while simultaneously giving them greater visibility of taxiing aircraft. Even beyond these benefits, however, the system described here will also be seen to have further potential benefits, such as for evaluating the effects of constraints upon the departure system or the flexibility of holding area structures

Enhancing decision support systems for airport ground movement

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. The ground movement problem forms the link between other airside problems at an airport, such as arrival sequencing, departure sequencing, gate/stand allocation and stand holding. The purpose of this thesis is to contribute to airport ground movement research through obtaining a better understanding of the problem and producing new models and algorithms for three sub-problems. Firstly, many stakeholders at an airport can benefit from more accurate taxi time predictions. This thesis focuses upon this aim by analysing the important factors affecting taxi times for arrivals and departures and by comparing different regression models to analyse which one performs the best for this particular task. It was found that incorporating the information of the airport layout could significantly improve the accuracy and that a TSK fuzzy rule-based system outperformed other approaches. Secondly, a fast and flexible decision support system is introduced which can help ground controllers in an airport tower to make better routing and scheduling decisions and can also absorb as much of the waiting time as possible for departures at the gate/stand, to reduce the fuel burn and environmental impact. The results show potential maximum savings in total taxi time of about 30.3%, compared to the actual performance at the airport. Thirdly, a new research direction is explored which analyses the trade-off between taxi time and fuel consumption during taxiing. A sophisticated new model is presented to make such an analysis possible. Furthermore, this research provides the basis for integrating the ground movement problem with other airport operations. Datasets from Zurich Airport, Stockholm-Arlanda Airport, London Heathrow Airport and Hartsfield-Jackson Atlanta International Airport were utilised to test these sub-problems

Airport slot allocation

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Cost Optimization Modeling for Airport Capacity Expansion Problems in Metropolitan Areas

The purpose of this research was to develop a cost optimization model to identify an optimal solution to expand airport capacity in metropolitan areas in consideration of demand uncertainties. The study first analyzed four airport capacity expansion cases from different regions of the world to identify possible solutions to expand airport capacity and key cost functions which are highly related to airport capacity problems. Using mixedinteger nonlinear programming (MINLP), a deterministic optimization model was developed with the inclusion of six cost functions: capital cost, operation cost, delay cost, noise cost, operation readiness, and airport transfer (ORAT) cost, and passenger access cost. These six cost functions can be used to consider a possible trade-off between airport capacity and congestion and address multiple stakeholders’ cost concerns. This deterministic model was validated using an example case of the Sydney metropolitan area in Australia, which presented an optimal solution of a dual airport system along with scalable outcomes for a 50-year timeline. The study also tested alternative input values to the discount rate, operation cost, and passenger access costs to review the reliability of the deterministic model. Six additional experimental models were tested, and all models successfully yielded optimal solutions. The moderating effects of financial discount rate, airport operation cost, and passenger access costs on the optimal solution were quantitatively the same in presence of a deterministic demand profile. This deterministic model was then transformed into a stochastic optimization model to address concerns with the uncertainty of future traffic demand, which was further reviewed with three what-if demand scenarios of the Sydney Model: random and positive growth of traffic demand, normal distribution of traffic demand changes based on the historical traffic record of the Sydney region, and reflection of the current COVID- 19 pandemic situation. This study used a Monte Carlo simulation to address the uncertainty of future traffic demand as an uncontrollable input. The Sydney Model and three What-if Models successfully presented objective model outcomes and identified the optimal solutions to expand airport capacity while minimizing overall costs. The results of this work indicated that the moderating effect of traffic uncertainties can make a difference with an optimal solution. Therefore, airport decision-makers and airport planners should carefully consider the uncertainty factors that would influence the airport capacity expansion solution. This research demonstrated the effectiveness of combining MINLP and the Monte Carlo simulation to support a long-term strategic decision for airport capacity problems in metropolitan areas at the early stages of the planning process while addressing future traffic demand uncertainty. Other uncertainty factors, such as political events, new technologies, alternative modes of transport, financial crisis, technological innovation, and demographic changes might also be treated as uncontrollable variables to augment this optimization model

Enhancing decision support systems for airport ground movement

With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. The ground movement problem forms the link between other airside problems at an airport, such as arrival sequencing, departure sequencing, gate/stand allocation and stand holding. The purpose of this thesis is to contribute to airport ground movement research through obtaining a better understanding of the problem and producing new models and algorithms for three sub-problems. Firstly, many stakeholders at an airport can benefit from more accurate taxi time predictions. This thesis focuses upon this aim by analysing the important factors affecting taxi times for arrivals and departures and by comparing different regression models to analyse which one performs the best for this particular task. It was found that incorporating the information of the airport layout could significantly improve the accuracy and that a TSK fuzzy rule-based system outperformed other approaches. Secondly, a fast and flexible decision support system is introduced which can help ground controllers in an airport tower to make better routing and scheduling decisions and can also absorb as much of the waiting time as possible for departures at the gate/stand, to reduce the fuel burn and environmental impact. The results show potential maximum savings in total taxi time of about 30.3%, compared to the actual performance at the airport. Thirdly, a new research direction is explored which analyses the trade-off between taxi time and fuel consumption during taxiing. A sophisticated new model is presented to make such an analysis possible. Furthermore, this research provides the basis for integrating the ground movement problem with other airport operations. Datasets from Zurich Airport, Stockholm-Arlanda Airport, London Heathrow Airport and Hartsfield-Jackson Atlanta International Airport were utilised to test these sub-problems