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H. Udluft
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In this work,we demonstrate that decentralized control can result in stable, efficient, and robust operations in the Air Transportation System. We implement decentralized control for aircraft taxiing operations and use Agent-Based Modeling and Simulation to analyze the resulting system behavior Decentralized controllers at each intersection of the airport taxiway system autonomously guide aircraft through the system. They achieve conflict-free operations with a minimum amount of information, coordinate their actions to improve system performance, and can dynamically accommodate disruptions such as runway configuration changes.
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In this work,we demonstrate that decentralized control can result in stable, efficient, and robust operations in the Air Transportation System. We implement decentralized control for aircraft taxiing operations and use Agent-Based Modeling and Simulation to analyze the resulting system behavior Decentralized controllers at each intersection of the airport taxiway system autonomously guide aircraft through the system. They achieve conflict-free operations with a minimum amount of information, coordinate their actions to improve system performance, and can dynamically accommodate disruptions such as runway configuration changes.
This article presents a retirement analysis model for aircraft fleets. By employing a greedy algorithm, the presented solution is capable of identifying individually weak assets in a fleet of aircraft with inhomogeneous historical utilization. The model forecasts future retirement scenarios employing user-defined decision periods, informed by a cost function, a utility function and demographic inputs to the model. The model satisfies first-order necessary conditions and uses cost minimization, utility maximization or a combination of the 2 as the objective function. This study creates a methodology for applying a greedy algorithm to a military fleet retirement scenario and then uses the United States Air Force A-10 Thunderbolt II fleet for model validation. It is shown that this methodology provides fleet managers with valid retirement options and shows that early retirement decisions substantially impact future fleet cost and utility.
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This article presents a retirement analysis model for aircraft fleets. By employing a greedy algorithm, the presented solution is capable of identifying individually weak assets in a fleet of aircraft with inhomogeneous historical utilization. The model forecasts future retirement scenarios employing user-defined decision periods, informed by a cost function, a utility function and demographic inputs to the model. The model satisfies first-order necessary conditions and uses cost minimization, utility maximization or a combination of the 2 as the objective function. This study creates a methodology for applying a greedy algorithm to a military fleet retirement scenario and then uses the United States Air Force A-10 Thunderbolt II fleet for model validation. It is shown that this methodology provides fleet managers with valid retirement options and shows that early retirement decisions substantially impact future fleet cost and utility.