Abstract
In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involvement of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS) and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective.
Highlights
Air Traffic Management (ATM) is a complex socio-technical system composed of multiple entities and physical/human actors
On top of time events in traditional Discrete Event Simulation (DES) systems, the simulation is driven by the so-called traffic events, emulating human and technical-component reactions in the face of the operational context evolution—e.g. an aircraft is approaching, entering or leaving a sector, a potential conflict has been detected by the Air Traffic Controllers (ATCOs) or by the supporting tools, a communication request has been received from another agent, etc
WORK We presented an ABMS-based evolutionary architecture designed for the evaluation and optimization of ATM systems
Summary
Air Traffic Management (ATM) is a complex socio-technical system composed of multiple entities and physical/human actors. We designed a distributed simulation-based evolutionary optimization architecture which enables what-if analyses via an agent-based simulation model, and allows to evaluate objective measures (such as the timeliness of the flights in a set of sectors), as well as subjective measures related to human behavior—in particular the behavior of Air Traffic Controllers (ATCOs). On top of time events in traditional DES systems (e.g. sector monitoring is continuously executed), the simulation is driven by the so-called traffic events, emulating human and technical-component reactions in the face of the operational context evolution—e.g. an aircraft is approaching, entering or leaving a sector, a potential conflict has been detected by the ATCO or by the supporting tools, a communication request has been received from another agent, etc.
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