Hybrid Genetic-cuckoo Search Algorithm for Solving Runway Dependent Aircraft Landing Problem

Abstract: As the demand for air transportation continues to grow, some flights cannot land at their preferred landing times because the airport is near its runway capacity. Therefore, devising a method for tackling the Aircraft Landing Problem (ALP) in order to optimize the usage of existing runways at airports is the focus of this study. This study, a hybrid Genetic-Cuckoo Search (GCS) algorithm for optimization the ALP with runway is proposed. The numerical results showed that the proposed GCS algorithm can effectively and efficiently determine the runway allocation, sequence and landing time for arriving aircraft for the three test cases by minimizing total delays under the separation constraints in comparison with the outcomes yielded by previous studies

A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem

Approximate Algorithms for the Combined arrival-Departure Aircraft Sequencing and Reactive Scheduling Problems on Multiple Runways

The problem addressed in this dissertation is the Aircraft Sequencing Problem (ASP) in which a schedule must be developed to determine the assignment of each aircraft to a runway, the appropriate sequence of aircraft on each runway, and their departing or landing times. The dissertation examines the ASP over multiple runways, under mixed mode operations with the objective of minimizing the total weighted tardiness of aircraft landings and departures simultaneously. To prevent the dangers associated with wake-vortex effects, separation times enforced by Aviation Administrations (e.g., FAA) are considered, adding another level of complexity given that such times are sequence-dependent. Due to the problem being NP-hard, it is computationally difficult to solve large scale instances in a reasonable amount of time. Therefore, three greedy algorithms, namely the Adapted Apparent Tardiness Cost with Separation and Ready Times (AATCSR), the Earliest Ready Time (ERT) and the Fast Priority Index (FPI) are proposed. Moreover, metaheuristics including Simulated Annealing (SA) and the Metaheuristic for Randomized Priority Search (Meta-RaPS) are introduced to improve solutions initially constructed by the proposed greedy algorithms. The performance (solution quality and computational time) of the various algorithms is compared to the optimal solutions and to each other. The dissertation also addresses the Aircraft Reactive Scheduling Problem (ARSP) as air traffic systems frequently encounter various disruptions due to unexpected events such as inclement weather, aircraft failures or personnel shortages rendering the initial plan suboptimal or even obsolete in some cases. This research considers disruptions including the arrival of new aircraft, flight cancellations and aircraft delays. ARSP is formulated as a multi-objective optimization problem in which both the schedule\u27s quality and stability are of interest. The objectives consist of the total weighted start times (solution quality), total weighted start time deviation, and total weighted runway deviation (instability measures). Repair and complete regeneration approximate algorithms are developed for each type of disruptive events. The algorithms are tested against difficult benchmark problems and the solutions are compared to optimal solutions in terms of solution quality, schedule stability and computational time

Optimisation du trafic aérien à l'arrivée dans la zone terminale et dans l'espace aérien étendu

Selon les prévisions à long terme du trafic aérien de l'Organisation de l'Aviation Civile Internationale (OACI) en 2018, le trafic mondial de passagers devrait augmenter de 4,2% par an de 2018 à 2038. Bien que l'épidémie de COVID-19 ait eu un impact énorme sur le transport aérien, il se rétablit progressivement. Dès lors, l'efficacité et la sécurité resteront les principales problématiques du trafic aérien, notamment au niveau de la piste qui est le principal goulot d'étranglement du système. Dans le domaine de la gestion du trafic aérien, la zone de manœuvre terminale (TMA) est l'une des zones les plus complexes à gérer. En conséquence, le développement d'outils d'aide à la décision pour gérer l'arrivée des avions est primordial. Dans cette thèse, nous proposons deux approaches d'optimisation qui visent à fournir des solutions de contrôle pour la gestion des arrivées dans la TMA et dans un horizon étendu intégrant la phase en route. Premièrement, nous abordons le problème d'ordonnancement des avions sous incertitude dans la TMA. La quantification et la propagation de l'incertitude le long des routes sont réalisées grâce à un modèle de trajectoire qui représente les informations temporelles sous forme de variables aléatoires. La détection et la résolution des conflits sont effectuées à des points de cheminement d'un réseau prédéfini sur la base des informations temporelles prédites à partir de ce modèle. En minimisant l'espérance du nombre de conflits, les vols peuvent être bien séparés. Outre le modèle proposé, deux autres modèles de la litérrature - un modèle déterministe et un modèle intégrant des marges de séparation - sont présentés comme références. Un recuit simulé (SA) combiné à une fenêtre glissante temporelle est proposé pour résoudre une étude de cas de l'aéroport de Paris Charles de Gaulle (CDG). De plus, un cadre de simulation basé sur l'approche Monte-Carlo est implémenté pour perturber aléatoirement les horaires optimisés des trois modèles afin d'évaluer leurs performances. Les résultats statistiques montrent que le modèle proposé présente des avantages absolus dans l'absorption des conflits en cas d'incertitude. Dans une deuxième partie, nous abordons un problème dynamique basé sur le concept de Gestion des Arrivées Étendue (E-AMAN). L'horizon E-AMAN est étendu jusqu'à 500 NM de l'aéroport de destination permettant ainsi une planification anticipée. Le caractère dynamique est traitée par la mise à jour périodique des informations de trajectoires réelles sur la base de l'approche par horizon glissant. Pour chaque horizon temporel, un sous-problème est établi avec pour objectif une somme pondérée de métriques de sécurité du segment en route et de la TMA. Une approche d'attribution dynamique des poids est proposée pour souligner le fait qu'à mesure qu'un aéronef se rapproche de la TMA, le poids de ses métriques associées à la TMA devrait augmenter. Une étude de cas est réalisée à partir des données réelles de l'aéroport de Paris CDG. Les résultats finaux montrent que grâce à cet ajustement anticipé, les heures d'arrivée des avions sont proches des heures prévues tout en assurant la sécurité et en réduisant les attentes. Dans la troisième partie de cette thèse, on propose un algorithme qui accélère le processus d'optimisation. Au lieu d'évaluer les performances de tous les aéronefs, les performances d'un seul aéronef sont concentrées dans la fonction objectif. Grâce à ce changement, le processus d'optimisation bénéficie d'une évaluation d'objectif rapide et d'une vitesse de convergence élevée. Afin de vérifier l'algorithme proposé, les résultats sont analysés en termes de temps d'exécution et de qualité des résultats par rapport à l'algorithme utilisé à l'origine.According to the long term air traffic forecasts done by International Civil Aviation Organization (ICAO) in 2018, global passenger traffic is expected to grow by 4.2% annually from 2018 to 2038 using the traffic data of 2018 as a baseline. Even though the outbreak of COVID-19 has caused a huge impact on the air transportation, it is gradually restoring. Considering the potential demand in future, air traffic efficiency and safety will remain critical issues to be considered. In the airspace system, the runway is the main bottleneck in the aviation chain. Moreover, in the domain of air traffic management, the Terminal Maneuvering Area (TMA) is one of the most complex areas with all arrivals converging to land. This motivates the development of suitable decision support tools for providing proper advisories for arrival management. In this thesis, we propose two optimization approaches that aim to provide suitable control solutions for arrival management in the TMA and in the extended horizon that includes the TMA and the enroute phase. In the first part of this thesis, we address the aircraft scheduling problem under uncertainty in the TMA. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models - the deterministic model and the model that incorporates separation buffers - are presented as benchmarks. Simulated annealing (SA) combined with the time decomposition sliding window approach is used for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented to randomly perturb the optimized schedules of the three models so as to evaluate their performances. Statistical results show that the proposed model has absolute advantages in conflict absorption when uncertainty arises. In the second part of this thesis, we address a dynamic/on-line problem based on the concept of Extended Arrival MANagement (E-AMAN). The E-AMAN horizon is extended up to 500NM from the destination airport so as to enhance the cooperation and situational awareness of the upstream sector control and the TMA control. The dynamic feature is addressed by periodically updating the real aircraft trajectory information based on the rolling horizon approach. For each time horizon, a sub-problem is established taking the weighted sum of safety metrics in the enroute segment and in the TMA as objective. A dynamic weights assignment approach is proposed to emphasize the fact that as an aircraft gets closer to the TMA, the weight for its metrics associated with the TMA should increase. A case study is carried out using the real arrival traffic data of the Paris CDG airport. Final results show that through early adjustment, the arrival time of the aircraft can meet the required schedule for entering the TMA, thus ensuring overall safety and reducing holding time. In the third part of this thesis, an algorithm that expedites the optimization process is proposed. Instead of evaluating the performance of all aircraft, single aircraft performance is focused and a corresponding objective function is created. Through this change, the optimization process benefits from fast evaluation of objective and high convergence speed. In order to verify the proposed algorithm, results are analyzed in terms of execution time and quality of result compared to the originally used algorithm

Modelling and Solving Decentralized and Dynamic Aircraft Landing Scheduling Problems

Aircraft landing problem (ALP) is considered as a scheduling problem where aircrafts are sequenced and allocated with appropriate time slots. In this thesis ALP problem is investigated where several constraints such as aircraft’s landing time windows, minimum separation time and position shifting constraints are taken into consideration. Existing approaches such as optimized solution based methods and heuristic methods to tackle different aspects of the problem are reviewed, and a static mathematical model is studied. The mathematical model is solved and verified using random data generated from simulation. The data are generated based on Pierre Elliott Trudeau International Airport (YUL) in Montreal, Quebec, Canada as well as from relevant data base library. AnyLogictm software was used to simulate aircraft landing operations in a runway environment. An agent based simulation was designed to include the dynamic event of aircrafts arrivals to the runway system. In the agent based system, an iterative bidding framework is used to generate flight landing schedule in a decentralized environment. In the decentralized environment, we consider each flight as a self-interest agent competing with other flights to get the most appropriate landing time. The efficiency of the decentralized approach is also studied. The results of the decentralized approach are compared with the centralized ALP solution. The results show that the agent based solution approach is able to generate reasonable landing comparing to optimal aircraft landing schedule from the centralized ALP model

Benders' decomposition algorithm to solve bi-level bi-objective scheduling of aircrafts and gate assignment under uncertainty

Abstract Management and scheduling of flights and assignment of gates to aircraft play a significant role in improving the procedure of the airport, due to the growing number of flights, decreasing the flight times. This research addresses assigning and scheduling of runways and gates in the main airport simultaneously. Moreover, this research considers the unavailability of runway's constraint and the uncertain parameters relating to both areas of runway and gate assignment. The proposed model is formulated as a comprehensive bi-level bi-objective problem.The leader's objective function minimizes the total waiting time for runways and gates for all aircrafts based on their importance coefficient. Meanwhile, the total distance traveled by all passengers in the airport terminal is minimized by a follower's objective function. To solve the proposed model, the decomposition approach based on Benders' decomposition method is applied. Empirical data are used to show the validation and application of our model. A comparison shows the effectiveness of the proposed model and its significant impact on cost decreasing

Maximising runway capacity by mid-term prediction of runway configuration and aircraft sequencing using machine learning

Maximising runway capacity is one of the essential measures to meet the growing traffic demand. Runway capacity maximisation is an open challenge in the literature due to a complex and non-linear interplay of many factors which are stochastic in nature such as wind, weather, and arrival and departure sequence of aircraft. However, an effective sequencing of arrivals and departures may condense the service time on runway, thereby generating opportunities for new landing or take-off slots, which may maximise the runway capacity. In addition, sequencing of arrivals and departures optimised for a predicted runway configuration, given the weather and wind conditions, may lead to maximising the runway capacity. First, I develop an optimisation method, using aircraft position shifting and path-planning, for aircraft sequencing for a single runway airport. The proposed method can provide an optimal aircraft sequence, for both arrivals and departures, such that it minimises the total inter-arrival and departure times and, consequently, maximises the runway throughput. The proposed method implements several arrival/departure sequencing strategies, i.e., constraint position-shifting with one, two and N-positions, and First Come First Serve (FCFS) in order to obtain an optimal sequence (i.e., a sequence with the lowest process time). The novelty of the sequencing model is to incorporate the Standard Terminal Arrival Routes (STAR) for path planning and sequencing of arriving aircraft at Final Approach Fix (FAF) and departing aircraft sequence at the runway threshold. The simulation result demonstrates that the model can increase up to 15% of the runway capacity compared with the commonly used aircraft sequencing technique (i.e., FCFS). Second, a runway configuration (i.e., a set of runways active in a specific period in a multi-runaway system) plays a vital role in determining runway capacity. Thus, I developed an evolutionary computation (Cooperative Co-evolutionary with Genetic Algorithm) algorithm for determining which runway configuration is most suitable for processing a given optimal aircraft sequence (arrival-departure), such that the runway capacity is maximised in a multi-runway system. The proposed algorithm models and uses Runway Configuration Capacity Envelopes (RCCEs) which defines arrival and departure throughputs. RCCE helps in identifying the unique capacity constraints based on which runways are used, for example, arrivals/departures or both. In the proposed evolutionary algorithm (CCoGA), the runway configuration and aircraft sequence are modelled as two species which interacts and evolve co-operatively to yield the best populations (combination) for maximising runway capacity. The fitness function for the optimal sequence species is to reduce the total process time for a given runway configuration, while fitness function for the runway configuration species is to maximises the total capacity for the given optimal sequence. The simulation results show that CCoGA can provide trade-off solutions with multiple runway configurations, for a given arrival-departure sequence, which can lead to capacity maximisation. Third, the weather conditions at an airport play a major role in determining the runway configuration which then has a significant impact on its runway capacity. Typically, aircraft take-off and landing operations use the runways which are most closely aligned with the wind direction, speed and other factors (e.g., cloud ceiling, visibility). However, selecting a runway configuration is a challenging task because it requires not only wind/weather (current and predicted) conditions but also the arrival/departure sequence (active and anticipated) at an airport. Predicting a suitable runway configuration under the operating conditions and a given traffic distribution may be useful for maximising the runway capacity. To achieve that, I first propose a Machine Learning (ML) model for predicting a suitable runway configuration given wind/weather and arrival/departure sequence. The simulation results demonstrate the accuracy of the prediction (98%) and usefulness of the ML techniques for assisting Air Traffic Controllers (ATCs) to choose certain runway configuration based on real-world weather data. In the final part of this thesis, I extend the ML model for forecasting runway configuration and develop a capacity estimation model for estimating associated capacity in a medium-term horizon (6 hours). The proposed model incorporates influencing factors (i.e., wind, visibility, cloud ceiling, and operation time) as well as all possible runway layouts for predicting the most suitable configuration. As a case study for Amsterdam Schiphol Airport, one month of airport weather data and associated runway configurations are processed to train and test the developed ML model. The results, on two days of sample traffic data, demonstrate the prediction accuracy of the ML model of up to 98%. Also, it is demonstrated that the ML predicted configuration can accommodate an additional number of flights (i.e., up to 20 flights) within one hour. This shows the viability and benefits of using ML approach for maximising runway capacity

Multi-fidelity modelling approach for airline disruption management using simulation

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%

Contributions to the Optimisation of aircraft noise abatement procedures

Tot i que en les últimes dècades la reducció del soroll emès pels avions ha estat substancial, el seu impacte a la població ubicada a prop dels aeroports és un problema que encara persisteix. Contenir el soroll generat per les operacions d'aeronaus, tot assumint al mateix temps la creixent demanda de vols, és un dels principals desafiaments a que s'enfronten les autoritats aeroportuàries, els proveïdors de serveis per a la navegació aèria i els operadors de les aeronaus. A part de millorar l'aerodinàmica o les emissions sonores de les aeronaus, l'impacte acústic de les seves operacions es pot reduir també gràcies a la definició de nous procediments de vol més òptims. Aquests procediments s'anomenen generalment Procediments d'Atenuació de Soroll (PAS) i poden incloure rutes preferencials de vol (a fi d'evitar les zones poblades) i també perfils de vol verticals optimitzats. Els procediments actuals per a la reducció de soroll estan molt lluny de ser els òptims. En general, la seva optimització no és possible a causa de les limitacions d'avui en dia en els mètodes de navegació, els equips d'aviònica i la complexitat present en alguns espais aeris. D'altra banda, molts PAS s'han dissenyat de forma manual per un grup d'experts i amb l'ajuda de diverses iteracions. Tot i això, en els propers anys s'esperen nous sistemes d'aviònica i conceptes de gestió del trànsit aeri que permetin millorar el disseny d'aquests procediments, fent que siguin més flexibles. En els pocs casos on s'optimitzen PAS, se sol utilitzar una mètrica acústica en l'elaboració de les diferents funcions objectiu i per tant, no es tenen en compte les molèsties sonores reals. La molèstia és un concepte subjectiu, complexe i que depèn del context en que s'usa i la seva integració en l'optimització de trajectòries segueix essent un aspecte a estudiar.La present tesi doctoral es basa en el fet que en el futur serà possible definir trajectòries més flexibles i precises. D'aquesta manera es permetrà la definició de procediments de vol òptims des d'un punt de vista de molèsties acústiques. Així doncs, es considera una situació en que aquest tipus de procediments poden ser dissenyats de forma automàtica o semi-automàtica per un sistema expert basat en tècniques d'optimització i de raonament aproximat. Això serviria com una eina de presa de decisions per planificadors de l'espai aeri i dissenyadors de procediments. En aquest treball es desenvolupa una eina completa pel càlcul de PAS òptims. Això inclou un conjunt de models no lineals que tinguin en compte la dinàmica de les aeronaus, les limitacions de la trajectòria i les funcions objectiu. La molèstia del soroll es modela utilitzant tècniques de lògica difusa en funció del nivell màxim de so percebut, l'hora del dia i el tipus de zona a sobrevolar. Llavors, s'identifica i es formula formalment el problema com a un problema de control òptim multi-criteri. Per resoldre'l es proposa un mètode de transcripció directa per tal de transformar-lo en un problema de programació no lineal. A continuació s'avaluen una sèrie de tècniques d'optimització multi-objectiu i entre elles es destaca el mètode d'escalarització, el més utilitzat en la literatura. No obstant això, s'exploren diverses tècniques alternatives que permeten superar certs inconvenients que l'escalarització presenta. En aquest context, es presenten i proven tècniques d'optimització lexicogràfica, jeràrquica, igualitària (o min-max) i per objectius. D'aquest anàlisi es desprenen certes conclusions que permeten aprofitar les millors característiques de cada tècnica i formar finalment una tècnica composta d'optimització multi-objectiu. Aquesta última estratègia s'aplica amb èxit a un escenari real i complex, on s'optimitzen les sortides cap a l'Est de la pista 02 de l'aeroport de Girona. En aquest exemple, dos tipus diferents d'aeronaus volant a diferents períodes del dia són simulats obtenint, conseqüentment, diferents trajectòries òptimes.Aunque en las últimas décadas la reducción del ruido emitido por los aviones ha sido sustancial, su impacto en la población ubicada cerca de los aeropuertos es un problema persistente. Contener este ruido, asumiendo al mismo tiempo la creciente demanda de vuelos, es uno de los principales desafíos a que se enfrentan las autoridades aeroportuarias, los proveedores de servicios para la navegación y los operadores. Aparte de mejorar la aerodinámica o las emisiones sonoras de las aeronaves, su impacto acústico se puede reducir también gracias a la definición de nuevos procedimientos de vuelo optimizados. Éstos, se denominan generalmente Procedimientos de Atenuación de Ruido (PAR) y pueden incluir rutas preferenciales de vuelo (a fin de evitar las zonas pobladas) y también perfiles de vuelo optimizados.Los procedimientos actuales para la reducción de ruido están muy lejos de ser los óptimos. En general, su optimización no es posible debido a las limitaciones de hoy en día en los métodos de navegación, los equipos de aviónica y la complejidad presente en algunos espacios aéreos. Por otra parte, muchos PAR se han diseñado de forma manual por un grupo de expertos y con la ayuda de varias iteraciones. Sin embargo, en los próximos años se esperan nuevos sistemas de aviónica y conceptos de gestión del tráfico aéreo que permitan mejorar el diseño de estos procedimientos, haciendo que sean más flexibles. En los pocos casos donde se optimizan PAR, se suele utilizar una métrica acústica en la elaboración de las diferentes funciones objetivo y por lo tanto, no se tienen en cuenta las molestias sonoras reales. La molestia es un concepto subjetivo, complejo y que depende del contexto en que se usa y su integración en la optimización de trayectorias sigue siendo un aspecto a estudiar. La presente tesis doctoral se basa en el hecho de que en el futuro será posible definir trayectorias más flexibles y precisas. De esta manera se permitirá la definición de procedimientos de vuelo óptimos desde un punto de vista de molestias acústicas. Se considera una situación en que este tipo de procedimientos pueden ser diseñados de forma automática o semi-automática por un sistema experto basado en técnicas de optimización y de razonamiento aproximado. Esto serviría como una herramienta de toma de decisiones para planificadores del espacio aéreo y diseñadores de procedimientos.En este trabajo se desarrolla una herramienta completa para el cálculo de PAR óptimos. Esto incluye un conjunto de modelos no lineales que tengan en cuenta la dinámica de las aeronaves, las limitaciones de la trayectoria y las funciones objetivo. La molestia del ruido se modela utilizando técnicas de lógica difusa en función del nivel máximo de sonido percibido, la hora del día y el tipo de zona a sobrevolar. Entonces, se identifica y se formula formalmente el problema como un problema de control óptimo multi-criterio. Para resolverlo se propone un método de transcripción directa para transformarlo en un problema de programación no lineal. A continuación se evalúan una serie de técnicas de optimización multi-objetivo y entre ellas se destaca el método de escalarización, el más utilizado en la literatura. Sin embargo, se exploran diversas técnicas alternativas que permiten superar ciertos inconvenientes que la escalarización presenta. En este contexto, se presentan y prueban técnicas de optimización lexicográfica, jerárquica, igualitaria (o min-max) y por objetivos. De este análisis se desprenden ciertas conclusiones que permiten aprovechar las mejores características de cada técnica y formar finalmente una técnica compuesta de optimización multi-objetivo. Esta última estrategia se aplica con éxito en un escenario real y complejo, donde se optimizan las salidas hacia el Este de la pista 02 del aeropuerto de Girona. En este ejemplo, dos tipos diferentes de aeronaves volando a diferentes periodos del día son simulados obteniendo, consecuentemente, diferentes trayectorias óptimas.Despite the substantial reduction of the emitted aircraft noise in the last decades, the noise impact on communities located near airports is a problem that still lingers. Containing the sound generated by aircraft operations, while meeting the increasing demand for aircraft transportation, is one of the major challenges that airport authorities, air traffic service providers and aircraft operators may deal with. Aircraft noise can be reduced by improving the aerodynamics of the aircraft, the engine noise emissions but also in designing new optimised flight procedures. These procedures, are generally called Noise Abatement Procedures (NAP) and may include preferential routings (in order to avoid populated areas) and also schedule optimised vertical flight path profiles. Present noise abatement procedures are far from being optimal in regards to minimising noise nuisances. In general, their optimisation is not possible due to the limitations of navigation methods, current avionic equipments and the complexity present at some terminal airspaces. Moreover, NAP are often designed manually by a group of experts and several iterations are needed. However, in the forthcoming years, new avionic systems and new Air Traffic Management concepts are expected to significantly improve the design of flight procedures. This will make them more flexible, and therefore will allow them to be more environmental friendly. Furthermore, in the few cases where NAP are optimised, an acoustical metric is usually used when building up the different optimisation functions. Therefore, the actual noise annoyance is not taken into account in the optimisation process. The annoyance is a subjective, complex and context-dependent concept. Even if sophisticated noise annoyance models are already available today, their integration into an trajectory optimisation framework is still something to be further explored. This dissertation is mainly focused on the fact that those precise and more flexible trajectories will enable the definition of optimal flight procedures regarding the noise annoyance impact, especially in the arrival and departure phases of flights. In addition, one can conceive a situation where these kinds of procedures can be designed automatically or semi-automatically by an expert system, based on optimisation techniques and approximate reasoning. This would serve as a decision making tool for airspace planners and procedure designers.A complete framework for computing optimal NAP is developed in this work. This includes a set of nonlinear models which take into account aircraft dynamics, trajectory constraints and objective functions. The noise annoyance is modelled by using fuzzy logic techniques in function of the perceived maximum sound level, the hour of the day and the type of over-flown zone. The problem tackled, formally identified and formulated as a multi-criteria optimal control problem, uses a direct transcription method to transform it into a Non Linear Programming problem. Then, an assessment of different multi-objective optimisation techniques is presented. Among these techniques, scalarisation methods are identified as the most widely used methodologies in the present day literature. Yet, in this dissertation several alternative techniques are explored in order to overcome some known drawbacks of this technique. In this context, lexicographic, hierarchical, egalitarian (or min-max) and goal optimisation strategies are presented and tested. From this analysis some conclusions arise allowing us to take advantage of the best features of each optimisation technique aimed at building a final compound multi-objective optimisation strategy. Finally, this strategy is applied successfully to a complex and real scenario, where the East departures of runway 02 at the airport of Girona (Catalonia, Spain) are optimised. Two aircraft types are simulated at different periods of the day obtaining different optimal trajectories.Postprint (published version

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