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Abstract

Airport capacity, expressed as the maximum number of air tra�c movements that can be accommodated during a given period of time under given conditions, has become a hard constraint to the air transportation, due to the scarce amount of resources on the ground and restrictions in the airspace. Usually the problem of capacity at airports is studied separating airspace operations from ground operations, but it is evident that the two areas are tied to each other. This work aims at developing a simulation model that takes into account both airspace and ground operations. The approach used is a divide and conquer approach, which allows the combination of four di�erent models. The four models refer to the airside, and airspace operations. This approach allows to evaluate the system from di�erent angles depending on the scope of the study, the results show the analytic potential of this approach.

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... These simulation software allow analysts to be more flexible when building the logic of the system, contrary to specific simulation software, where analysts are restricted to use the predefined objects and logic. In [16] a DES model for the evaluation of the integrated operations of both airspace and airside was proposed, by combining four different modules, each of them representing a specific operation (airspace aircraft sequencing, turn around, taxiway routing, runway movements). In this way an analysis of the airport performance from a holistic view was realized, identifying potential problems that might affect the capacity of the airport. ...
... This program is used in the current study of the paper. In this work, a simulation model was developed, similarly as [16], however, in [16], the conflict detection and resolution (ECD&R) was not implemented, as it represents an innovative feature implemented in this work. An additional feature in which this work goes beyond the state of the art, is the inclusion of uncertainty in order to represent the variability of the operations both in the airspace and on the ground. ...
... This program is used in the current study of the paper. In this work, a simulation model was developed, similarly as [16], however, in [16], the conflict detection and resolution (ECD&R) was not implemented, as it represents an innovative feature implemented in this work. An additional feature in which this work goes beyond the state of the art, is the inclusion of uncertainty in order to represent the variability of the operations both in the airspace and on the ground. ...
Article
Airport capacity has become a constraint in the air transportation networks due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum potential conflicts. In this framework, airspace together with ground airport operations is considered. The conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways and as capacity overloads for taxiway network and terminals. The methodology proposed in this paper consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution, while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, and this is supported by a case study of Paris Charles de Gaulle Airport.
... The studies previously mentioned reveal that simulation can be a powerful tool for supporting decisions in the planning phase of large and costly infrastructure such as airports. However, its use in the planning phases of an aviation study is not common, although it is an approach that some participants and researchers are actively exploring [13,[16][17][18]. ...
... A DES simulation model has been developed with SIMIO. This was adopted because it allows the development through a bottom-up approach of a dynamic model [16]. The developed model has characteristics that are dynamic, stochastic and asynchronous. ...
... We present only a partial analysis of Model A, since the combinations of the input levels are too big to be included in this work and would be beyond the scope of the article. However, if the reader is interested in the complete analysis of Model A, we refer to [13,16]. In the following subsections, the sub models that compose Model A are presented and briefly discussed. ...
Article
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The aeronautical industry is expanding after a period of economic turmoil. For this reason, a growing number of airports are facing capacity problems that can sometimes only be resolved by expanding infrastructure, with the inherent risks that such decisions create. In order to deal with uncertainty at different levels, it is necessary to have relevant tools during an expansion project or during the planning phases of new infrastructure. This article presents a methodology that combines simulation approaches with different description levels that complement each other when applied to the development of a new airport. The methodology is illustrated with an example that uses two models for an expansion project of an airport in The Netherlands. One model focuses on the operation of the airport from a high-level position, while the second focuses on other technical aspects of the operation that challenge the feasibility of the proposed configuration of the apron. The results show that by applying the methodology, analytical power is enhanced and the risk of making the wrong decisions is reduced. We identified the limitations that the future facility will have and the impact of the physical characteristics of the traffic that will operate in the airport. The methodology can be used for tackling different problems and studying particular performance indicators to help decision-makers take more informed decisions.
... They used the ARENA (ARENA, 2018) general purpose simulation software to model and simulate the turnaround operations, the same simulation software has been used in the work of Norin et al. (2012), where they tested different scheduling alternatives for de-icing equipment and evaluated their impact on the overall airport performance in term of delays and waiting times. In the work of Scala et al. (2017), a DES model was developed by using the general-purpose simulation software SIMIO (SIMIO, 2018). In their work, they proposed a modular approach to simulate airspace and airside airport together. ...
... The simulation model was built according to the macroscopic level previously introduced, furthermore, it works in together with an optimization model for evaluating the performance of the solution obtained from it. In the simulation model, TMA operations were modeled similarly as the work of Scala et al. (2017), making sure that all the main rules about speed, altitude, and separation minima between aircraft were properly implemented. An additional feature was the development of conflict detection within the simulation model, allowing it to detect not only conflicts in the TMA, similarly as Zuniga et al. (2013), but also detecting conflicts for ground operations. ...
Thesis
L'augmentation constante du trafic aérien, spécialement en Europe, exerce une pression sur les aéroports, qui en conséquence sont souvent congestionnés. La zone aérienne entourant les aéroports, l'aire de manœuvre terminale (TMA), est particulièrement encombrée, puisqu'elle accueille tout le trafic aéroportuaire. Outre la zone aérienne, le partie sol fait aussi face à des problèmes d'encombrement, ainsi l'inefficacité des opérations en zone aérienne est transférée au sol. Cet encombrement des zones aériennes et terrestres des aéroports a pour conséquence de générer des retards, qui sont ensuite reportés sur les autres aéroports du réseau. Le problème d'encombrement affecte également la charge de travail des contrôleurs aériens qui doivent gérer ce large trafic. Cette thèse porte sur l'optimisation des opérations intégrées aux aéroports, en considérant l'aéroport d'un point de vue holistique et en incluant les activités aériennes et terrestres. Contrairement aux autres études dans ce domaine, cette thèse apporte sa contribution en appuyant les décisions des contrôleurs aériens en terme de séquencement des avions et en atténuant l'encombrement de la partie sol des aéroports. Les activités terrestres et aériennes peuvent être abordées avec deux différents niveaux d'abstractions, macroscopique, ou microscopique, en raison de différent délais de prise de décision. Dans cette thèse, les activités sont modélisées au niveau macroscopique. Le problème est formulé comme un modèle d'optimisation en identifiant une fonction objective qui prend en compte le nombre de conflits dans l'espace aérien et la surcharge au sol des aéroports; contraintes données par la régulation sur le minimum de séparation entre des avions consécutifs dans la zone aérienne et sur la piste de décollage; variables de décision liées au temps d'entrée de l'avion et à la vitesse d'entrée dans l'espace arien, au choix de la piste d'atterrissage et de la piste au départ et à l'heure de push-back. Le modèle d'optimisation est résolu en implémentant une approche par fenêtre glissante et par une version adaptée de la métaheuristique de recuit simulé. Des incertitudes sont ajoutées dans les activités en développant un modèle de simulation et en incluant des variables stochastiques représentant des sources d'incertitudes comme une variation de l'heure d'entrée dans l'espace aérien de l'aéroport, une variation de l'heure moyenne de temps du roulage ou encore une variation dans l'heure de push-back des avions.
... They used the ARENA (ARENA, 2018) general purpose simulation software to model and simulate the turnaround operations, the same simulation software has been used in the work of Norin et al. (2012), where they tested different scheduling alternatives for de-icing equipment and evaluated their impact on the overall airport performance in term of delays and waiting times. In the work of Scala et al. (2017), a DES model was developed by using the general-purpose simulation software SIMIO (SIMIO, 2018). In their work, they proposed a modular approach to simulate airspace and airside airport together. ...
... The simulation model was built according to the macroscopic level previously introduced, furthermore, it works in together with an optimization model for evaluating the performance of the solution obtained from it. In the simulation model, TMA operations were modeled similarly as the work of Scala et al. (2017), making sure that all the main rules about speed, altitude, and separation minima between aircraft were properly implemented. An additional feature was the development of conflict detection within the simulation model, allowing it to detect not only conflicts in the TMA, similarly as Zuniga et al. (2013), but also detecting conflicts for ground operations. ...
Thesis
The constant growth of air traffic, especially in Europe, is putting pressure on airports, which, in turn, are suffering congestion problems. The airspace surrounding airport, terminal manoeuvring area (TMA), is particularly congested, since it accommodates all the converging traffic to and from airports. Besides airspace, airport ground capacity is also facing congestion problems, as the inefficiencies coming from airspace operations are transferred to airport ground and vice versa. The main consequences of congestion at airport airspace and ground, is given by the amount of delay generated, which is, in turn, transferred to other airports within the network. Congestion problems affect also the workload of air traffic controllers that need to handle this big amount of traffic.This thesis deals with the optimization of the integrated airport operations, considering the airport from a holistic point of view, by including operations such as airspace and ground together. Unlike other studies in this field of research, this thesis contributes by supporting the decisions of air traffic controllers regarding aircraft sequencing and by mitigating congestion on the airport ground area. The airport ground operations and airspace operations can be tackled with two different levels of abstractions, macroscopic or microscopic, based on the time-frame for decision-making purposes. In this thesis, the airport operations are modeled at a macroscopic level.The problem is formulated as an optimization model by identifying an objective function that considers the amount of conflicts in the airspace and capacity overload on the airport ground; constraints given by regulations on separation minima between consecutive aircraft in the airspace and on the runway; decision variables related to aircraft entry time and entry speed in the airspace, landing runway and departing runway choice and pushback time. The optimization model is solved by implementing a sliding window approach and an adapted version of the metaheuristic simulated annealing. Uncertainty is included in the operations by developing a simulation model and by including stochastic variables that represent the most significant sources of uncertainty when considering operations at a macroscopic level, such as deviation from the entry time in the airspace, deviation in the average taxi time and deviation in the pushback time. In this thesis, optimization and simulation techniques are combined together by developing two methods that aim at improving the solution robustness and feasibility. The first method acts as a validation tool for the optimized solution, and it improves the robustness of solution by iteratively fine-tuning some of the optimization model input parameters. The second method embeds the optimization in a simulation environment by taking full advantage of the sliding window approach and creating a loop for a continuous improvement of the optimized solution at each window of the sliding window approach. Both methods prove to be effective by improving the performance, lowering the total amount of conflicts up to 23.33% for the first method and up to 11.2% for the second method, however, in contrast to the deterministic method, the two methods they are not able to achieve a conflict-free scenario due to the effect of uncertainty.In general, the research conducted in this thesis highlights that uncertainty is a factor that affects to a large extent the feasibility of optimized solution when applied to real-world instances, and it, moreover, confirms that using simulation together with optimization has the potentiality to ivdeal with uncertainty. The framework developed can be potentially applied to similar problems and different optimization solving methods can be adapted to it.
... The aforementioned works focus on specific airport operations, such as airspace and/or ground, and do not consider airport operations in a holistic view. In Scala et al (2017a), a model that considered airport operations with airspace and ground operations together was made. A downside of this work, was that the Scala, Mujica, Ma, and Delahaye model could not be used as a general approach since different abstraction levels were used for approaching a particular problem. ...
Conference Paper
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In this paper, a general approach for modeling airport operations is presented. Airport operations have been extensively studied in the last decades ranging from airspace, airside and landside operations. Due to the nature of the system, simulation techniques have emerged as a powerful approach for dealing with the variability of these operations. However, in most of the studies, the different elements are studied in an individual fashion. The aim of this paper, is to overcome this limitation by presenting a methodological approach where airport operations are modeled together, such as airspace and airside. The contribution of this approach is that the resolution level for the different elements is similar therefore the interface issues between them is minimized. The framework can be used by practitioners for simulating complex systems like airspace-airside operations or multi-airport systems. The framework is illustrated by presenting a case study analyzed by the authors.
... The studies previously mentioned reveal that simulation can be a powerful tool for supporting decisions in the planning phase of big and costly infrastructures such as airports. However, its use in the planning phases of an aviation study is not common practice, but it is an approach that the authors are actively exploring as it can be perceived in Mujica (2015), Mujica et al. (2015), Scala et al. (2016), Mota and Scala (2017) and Mota et al. (2017). For this reason, the aim of the current work is to present the intermediate phase of the simulation-based methodology ( Figure 2) for revealing the operational potential of a situation that has probability of happening in reality. ...
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The airport of Mexico City has been declared saturated for most of the day. For that reason, the Mexican government announced a couple of years ago the construction of a completely new one which is supposed to be operative in 2020 in its first phase. However, the technical issues and the economic downturn in the country jeopardize the project; for that reason, it is important to have alternatives that allow investing in a progressive fashion so that the investments are not lost or end up in useless infrastructure like the ones that have taken place in other parts of the world. The current work presents a simulation-based study of the alternative of using one of the runways of the new airport in a remote fashion in case the original project is delayed or even cancelled. The results indicate that the proposed infrastructure alleviates the congestion problem in the current airport, and at the same time allows the traffic growth with performance indicators similar to airports that have remote runways as it is the case of Schiphol in The Netherlands.
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Amsterdam Schiphol (AMS) is the main airport in the Netherlands and it was the fifth busiest airport in Europe in 2014 in terms of passenger traffic. Due to environmental reasons, the capacity at AMS is limited to 510,000 air traffic movements per year. In 2014 there were 438,296 movements at the airport representing 86% of the imposed cap, therefore Schiphol Group decided to divert the non-hub related traffic to the regional airport in Lelystad. This airport will be upgraded to handle commercial traffic, manly low cost carriers. Turnaround times are one of the important factors that will determine the attractiveness of the airport for potential airlines to start operations there. In this work the authors present a simulation-based analysis of the turnaround time performance using an approach that allows modeling the airport in a dynamic way taking into account the variability and uncertainty inherent in the system.
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Assigning commercial service aircrafts to the available gates at an airport depends on flights scheduled, their actual behavior relative to those schedules, aircraft servicing requirements and capacities of ramp facilities. Flight delays, severe weather, or equipment failures can disrupt the planned schedules, and compound the difficulty of maintaining smooth station operations. A mixed-binary mathematical model with a quadratic function for minimizing the variance of idle times at the gates is proposed to make the initial assignments insensitive to variations in flight schedules.Experimental results with a branch and bound algorithm indicate that more computational effort is required to assign flights optimally over low utilized gates. Furthermore, a heuristic employing the priority function which considers additionally the possible idle times from the future assignments outperforms the others, significantly. Over the real data obtained from the Saudi Arabian Airlines, average 87.4% and 76.2% of improvements can be obtained on the number of aircrafts assigned initially to remote area and that towed from their assigned gates during the real-time implementation, respectively.
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The efficient operation of airports, and runways in particular, is critical to the throughput of the air transportation system as a whole. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations of efficiency, safety, and equity among airlines. One approach to runway scheduling that arises from operational and fairness considerations is that of constrained position shifting (CPS), which requires that an aircraft's position in the optimized sequence not deviate significantly from its position in the first-come-first-served sequence. This paper presents a class of scalable dynamic programming algorithms for runway scheduling under constrained position shifting and other system constraints. The results from a prototype implementation, which is fast enough to be used in real time, are also presented.
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