Setorização - Melhoria de método baseado em Eletromagnetismo

Ao longo desta dissertação foi utilizado um método baseado na Lei de Coulomb para a criação de setores. Este método foi aplicado num problema CLRP com 50 clientes e 5 potenciais centros de distribuição (adaptado de Christofides and Eilon (1969)). Após a criação dos setores foi aplicada uma meta-heurística, baseada em padrões da natureza, para o estabelecimento de rotas: particle swarm optimization. As diversas soluções obtidas estão a ser comparadas entre elas e com outro artigo que resolve o mesmo problema com uma abordagem diferente

Airspace analysis for greener operations: towards more adoptability and predictability of continuous descent approach (cda)

Continuous Descent Approach (CDA), also known as Optimized Profile Descent (OPD), is the advanced flight technique for commercial aircraft to descend continuously from cruise altitude to Final Approach Fix (FAF) or touchdown without level-offs and with- or near-idle thrust setting. Descending using CDA, aircraft stays as high as possible for longer time thereby expanding the vertical distance between aircraft\u27s sources of noise and ground, and thus significantly reducing the noise levels for populated areas around airports. Also, descending with idle engines, fuel burn is reduced resulting in reduction of harmful emissions to the environment and fuel consumption to air carriers. Due to safety considerations, CDA procedures may require more separation between aircraft, which could reduce the full utilization of runway capacity. Thus, CDA has been limited to low to moderate traffic levels at airports. Several studies in literature have used various approaches to present solutions to the problem of increasing the CDA implementation during periods of high traffic at airports. However, insufficient attention was given to define thresholds that would help Air Traffic Controllers (ATC) to manage and accommodate more CDA operations, strategically and tactically. Bridging this gap is the main intent of this work. This research focus is on increasing CDA operations at airports during high traffic levels by considering factors that impact its CDA adoption as they relate to airports\u27 demographics, and airspace around them {known as terminal maneuvering area (TMA)}. To capture the effect of these factors on CDA Adoptability (CDA-A), in general, and CDA Predictability (CDA-P), at the operational level, two (2) approaches are introduced. The CDA-A model defines and captures the maximum level of traffic threshold for CDA adoption. The model captures the factors affecting CDA in a single measure, which are designated collectively as the Probability of Blocking. It is defined as the fraction of time an aircraft\u27s request to embark on CDA is denied. The denial could emanate from safety concerns as well as other operational conditions, such as the congestion of the stacking space within the TMA. This metric should enhance ATC on the strategic level to increasing CDA operations during possibly higher traffic than normally the case. The other approach is for a CDA-P. This model is developed based on data-driven system approach. It extracts traffic features, such as aircraft type and speed, altitude, and rate of descent; from actual flights data to aid in further operational utilization of CDA in real time. By accurately predicting CDA instances during high traffic at airports, the CDA-P model should assist ATC manage adopting more CDA operations during periods of high demand. Through its framework, the CDA-P model utilizes Feature Engineering and Hierarchal Clustering Analysis, to facilitate descent profile visualization and labeling, for building, training, testing, and validation of CDA predictive models using Decision Trees with AdaBoost and Support Vector Machines (SVM). The CDA-P model is validated using actual flight data operated at Nashville Int\u27l Airport (BNA)

Full Automation of Air Traffic Management in High Complexity Airspace

The thesis is that automation of en-route Air Traffic Management in high complexity airspace can be achieved with a combination of automated tactic planning in a look-ahead time horizon of up to two hours complemented with automated tactic conflict resolution functions. The literature review reveals that no significant results have yet been obtained and that full automation could be approached with a complementary integration of automated tactic resolutions AND planning. The focus shifts to ‘planning for capacity’ and ‘planning for resolution’ and also – but not only – for ‘resolution’. The work encompasses a theoretical part on planning, and several small scale studies of empirical, mathematical or simulated nature. The theoretical part of the thesis on planning under uncertainties attempts to conceive a theoretical model which abstracts specificities of planning in Air Traffic Management into a generic planning model. The resulting abstract model treats entities like the planner, the strategy, the plan and the actions, always considering the impact of uncertainties. The work innovates in specifying many links from the theory to the application in planning of air traffic management, and especially the new fields of tactical capacity management. The second main part of the thesis comprises smaller self-containing works on different aspects of the concept grouped into a section on complexity, another on tactic planning actions, and the last on planners. The produced studies are about empirical measures of conflicts and conflict densities to get a better understanding of the complexity of air traffic; studies on traffic organisation using tactical manoeuvres like speed control, lateral offset and tactical direct using fast time simulation; and studies on airspace design like sector optimisation, dynamic sectorisation and its optimisation using optimisation techniques. In conclusion it is believed that this work will contribute to further automation attempts especially by its innovative focus which is on planning, base on a theory of planning, and its findings already influence newer developments

Airspace sectorization with constraints

We consider the Airspace Sectorization Problem (ASP) in which airspace has to be partitioned into a given number of sectors, each of which being assigned to a team of air traffic controllers. The objective is to minimize the coordination workload between adjacent sectors while balancing the total workload of controllers. Many specific constraints, including both geometrical and aircraft related constraints are taken into account. The problem is solved in a constraint programming framework. Experimental results show that our approach can be used on real life problems

Airspace sectorization with constraints

We consider the Airspace Sectorization Problem (ASP) in which airspace has to be partitioned into a given number of sectors, each of which being assigned to a team of air traffic controllers. The objective is to minimize the coordination workload between adjacent sectors while balancing the total workload of controllers. Many specific constraints, including both geometrical and aircraft related constraints are taken into account. The problem is solved in a constraint programming framework. Experimental results show that our approach can be used on real life problems