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.
Modern airport management is challenged by the task of operating aircraft parking positions most efficiently while complying with environmental policies, restrictions, schedule disruptions, and capacity limitations. This study proposes a novel framework for the stand allocation problem that uses a divide-and-conquer approach in combination with Bayesian modelling, simulation, and optimisation to produce less-pollutant solutions under realistic conditions. The framework presents three innovative aspects. First, inputs from the stochastic analysis module are used in a multivariate optimisation for generating variability-robust solutions. Second, a combination of optimisation and simulation is used to finely explore the impact of realistic uncertainty uncaptured by the framework. Lastly, the framework considers the role of human beings as the final control of operational conditions. A case study is presented as a proof of concept and demonstrates results achievable and benefits of the framework proposed. The experimental results demonstrate that the framework generates less-pollutant solutions under realistic conditions.
The Interoceanic corridor of Mexico stands as a pivotal infrastructure project poised to significantly enhance Mexico's national and regional economy. Anticipated to start the operations in 2025 under the auspice of the national government, this corridor represents a strategic counterpart to the Panama Canal, which faces capacity constraints due to climate change and environmental impacts. Positioned as a promising alternative for transporting goods from Asia to North America, this corridor will offer a new transport route, yet its real operational capacity and spatial impacts remains uncertain. In this paper, the authors undertake a preliminary, informed analysis leveraging publicly available data and other specific information about infrastructure capacities and economic environment to forecast the potential throughput of this corridor upon full operationalization and in the future. Applying simulation techniques, the authors simulate the future operations of the corridor according to different scenarios to offer insights into its potential capacity and impacts. Furthermore, the paper delves into the opportunities and challenges that are inherent in this project and gives a comprehensive analysis of its potential impact and implications.
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