Upravljanje putanjama vazduhoplova u kontroli letenja na pre-taktičkom i taktičkom nivou

Global air traffic demand is continuously increasing, and it is predicted to be tripled by 2050. The need for increasing air traffic capacity motivates a shift of ATM towards Trajectory Based Operations (TBOs). This implies the possibility to design efficient congestion-free aircraft trajectories more in advance (pre-tactical, strategic level) reducing controller’s workload on tactical level. As consequence, controllers will be able to manage more flights. Current flow management practices in air traffic management (ATM) system shows that under the present system settings there are only timid demand management actions taken prior to the day of operation such as: slot allocation and strategic flow rerouting. But the choice of air route for a particular flight is seen as a commercial decision to be taken by airlines, given air traffic control constraints. This thesis investigates the potential of robust trajectory planning (considered as an additional demand management action) at pre-tactical level as a mean to alleviate the en-route congestion in airspace. Robust trajectory planning (RTP) involves generation of congestion-free trajectories with minimum operating cost taking into account uncertainty of trajectory prediction and unforeseen event. Although planned cost could be higher than of conventional models, adding robustness to schedules might reduce cost of disruptions and hopefully lead to reductions in operating cost. The most of existing trajectory planning models consider finding of conflict-free trajectories without taking into account uncertainty of trajectory prediction. It is shown in the thesis that in the case of traffic disturbances, it is better to have a robust solution otherwise newly generated congestion problems would be hard and costly to solve. This thesis introduces a novel approach for route generation (3D trajectory) based on homotopic feature of continuous functions. It is shown that this approach is capable of generating a large number of route shapes with a reasonable number of decision variables. Those shapes are then coupled with time dimension in order to create trajectories (4D)...Globalna potražnja za vazdušnim saobraćajem u stalnom je porastu i prognozira se da će broj letova biti utrostručen do 2050 godine. Potreba za povećanjem kapaciteta sistema vazdušnog saobraćaja motivisala je promene u sistemu upravljanja saobraćajnim tokovima u kome će u budućnosti centralnu ulogu imati putanje vazduhoplova tzv. “trajectory-based” koncept. Takav sistem omogućiće planiranje putanja vazduhoplova koje ne stvaraju zagušenja u sistemu na pre-taktičkom nivou i time smanjiti radno opterećenje kontrolora na taktičkom nivou. Kao posledica, kontrolor će moći da upravlja više letova nego u današnjem sistemu. Današnja praksa upravljanja saobraćajnim tokovima pokazuje da se mali broj upravljačkih akcija primenjuje pre dana obavljanja letova npr.: alokacija slotova poletanja i strateško upravljanje saobraćajnim tokovima. Međutim izbor putanje kojom će se odviti let posmatra se kao komercijalna odluka aviokompanije (uz poštovanje postavljenih ograničenja od strane kontrole letenja) i stoga je ostavljen na izbor avio-kompaniji. Većina, do danas razvijenih, modela upravljanja putanjama vazduhoplova ima za cilj generisanje bez-konfliktnih putanja, ne uzimajući u obzir neizvesnost u poziciji vazduhoplova. U ovoj doktorskoj disertaciji ispitivano je planiranje robustnih putanja vazduhoplova (RTP) na pre-taktičkom nivou kao sredstvo ublažavanja zagušenja u vazdušnom prostoru . Robustno upravljanje putanjama vazduhoplova podrazumeva izbor putanja vazduhoplova sa minimalnim operativnim troškovima koje ne izazivaju zagušenja u vazdušnom prostoru u uslovima neizvesnosti buduđe pozicije vazduhoplova i nepredviđenih događaja. Iako predviđeni (planirani) operativni troškovi robustnih putanja mogu u startu biti veći od operativnih troškova bez-konfliktnih putanja, robusnost može uticati na smanjenje troškove poremećaja putanja jer ne zahteva dodatnu promenu putanja vazduhplova radi izbegavanja konfliktnih situacija na taktičkom nivou. To na kraju može dovesti i do smanjenja stvarnih operativnih troškova. U tezi je pokazano, da je u slučaju poremećaja saobraćaja bolje imati robustno rešenje (putanje), jer novo-nastali problem zagušenosti vazdušnog prostora je teško i skupo rešiti..

Domain-driven multiple-criteria decision-making for flight crew decision support tool

During the flight, the crew might consider modifying their planned trajectory, taking into account currently available information, such as an updated weather forecast report or the already accrued amount of delay. This modified planned trajectory translates into changes on expected fuel and flying time, which will impact the airline’s relevant performance indicators leading to a complex multiple-criteria decision-making problem. Pilot3, a project from the Clean Sky Joint Undertaking 2 under European Union’s Horizon 2020 research and innovation programme, aims to develop an objective optimisation engine to assist the crew on this process. This article presents a domain-driven approach for the selection of the most suitable multiple-criteria decision-making methods to be used for this optimisation framework. The most relevant performance indicators, based on airline’s objectives and policies, are identified as: meeting on-time performance, leading to a binary value in a deterministic scenario; and total cost, which can be disaggregated into sub-cost components. The optimisation process consists of two phases: first, Pareto optimal solutions are generated with a multi-objective optimisation method (lexicographic ordering); second, alternative trajectories are filtered and ranked using a combination of multi-criteria decision analysis methods (analytic hierarchy process and VIKOR). A realistic example of use shows the applicability of the process and studies the sensibility of the optimisation framework

Complexity challenges in ATM

After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise

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%

Application of stochastic programming techniques to airline scheduling

The goal of this project was to evaluate the effectiveness of stochastic programming techniques when applied to the airline scheduling process to reduce the effect of stochastic flight delays. A variety of traditional and stochastic programming models were developed for generating flight schedules. The resultant flight schedules were tested using simulations to evaluate their performance in real-world conditions with regard to flight delays, and their effects on the schedule’s operations. It was found that stochastic programming techniques were able to improve the delay recovery performance of the schedules at the cost of decreasing the schedule’s profit; and that flight schedules which are more dense with flight activity are affected more by the stochastic programming techniques. The use of stochastic programming techniques is recommended for the cases where an airline’s flight schedule has a high density of activity and the negative effects of flight delays needs to be minimized