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Analysing capacity challenges in the Multi-Airport System of Mexico City

The relentless growth in Mexico City’s aviation traffic has inevitably strained capacity development of its airport, raising thedilemma between the possible solutions. In the present study, Mexico’s Multi-Airport System is subjected to analysis by meansof multi-model simulation, focusing on the capacity-demand problem of the system. The methodology combines phases ofmodelling, data collection, simulation, experimental design, and analysis. Drawing a distinction from previous works involvingtwo-airport systems. It also explores the challenges raised by the Covid-19 pandemic in Mexico City airport operations, with adiscrete-event simulation model of a multi-airport system composed by three airports (MEX, TLC, and the new airport NLU).The study is including the latest data of flights, infrastructures, and layout collected in 2021. Therefore, the paper aims toanswer to the question of whether the system will be able to cope with the expected demand in a short-, medium-, and longtermby simulating three future scenarios based on aviation forecasts. The study reveals potential limitations of the system astime evolves and the feasibility of a joint operation to absorb the demand in such a big region like Mexico City.

Time to sweat the assets?

Airport Capacity Management Optimization vs Airline Fairness: an empirical study

This research aims to find relevant evidence on whether there is a link between air capacity management (ACM) optimization and airline operations, also considering the airline business model perspective. The selected research strategy includes a case study based on Paris Charles de Gaulle Airport to measure the impact of ACM optimization variables on airline operations. For the analysis we use historical data which allows us to evaluate to what extent the new schedule obtained from the optimized scenario disrupts airline planned operations. The results of this study indicate that ACM optimization has a substantial impact on airline operations. Moreover, the airlines were categorized according to their business model, so that the results of this study revealed which category was the most affected. In detail, this study revealed that, on the one hand, Full-Service Cost Carriers (FSCCs) were the most impacted and the presented ACM optimization variables had a severe impact on slot allocation (approximately 50% of slots lost), fuel burn accounted as extra flight time in the airspace (approximately 12 min per aircraft) and disrupted operations (approximately between 31% and 39% of the preferred assigned runways were changed). On the other hand, the comparison shows that the implementation of an optimization model for managing the airport capacity, leads to a more balanced usage of runways and saves between 7% and 8% of taxi time (which decreases fuel emission).

Is there enough capacity?

KLM is downsizing the full-freight cargo fleet in Schiphol Airport, for that reason it is important for the company and the airport to explore the consequences of moving the cargo transported by the full freighters into the bellies of the passenger flights. The consequences of this action in terms of capacity and requirements are still unknown. The current study illustrates how to analyse the uncertainty present in the system for identifying the limitations and potential consequences of the reduction of full freighter fleet. The options we identify for coping with the current demand is by adjusting their load factors or increase the number of flights. The current model includes the airside operation of the airport, the truck movements and the traffic that arrives at Schiphol which allows addressing the impact of uncertainties of the operation as well as the limitations and potential problems of the phasing-out action.

Air taffic management during rare events such as a pandemic

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways

An optimization-simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.