An Applied Optimization Framework for Distributed Air Transportation Environments

  • Thomas Castelli
  • Joshua Lee
  • Waseem Naqvi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


In a large-scale dynamic system with multiple distributed entities, each with their own set of interests, there is a need to find a globally acceptable and optimal solution state. This solution state is, by definition, efficient to all entities with respect to their own individual goals and to the system as a whole. In these dynamic environments, this solution state can be achieved by utilizing software techniques from the field of game theory in order to make optimal decisions. We present an application built upon a generalized optimization framework that can be applied to a number of domains, such as cargo or network traffic algorithms. In this research, we used a market-based approach to air traffic flow management through a modeling and simulation environment. The aim is to allow individual aircraft a certain degree of local autonomy, much like cars on a highway. Our system is able to cope in real time with failures such as node loss and adjust system parameters accordingly to optimize results based on the goals of the involved agents. We describe tradeoffs between different agent interaction frameworks with respect to their performance in market mechanism auctions. We also discuss lessons learned while implementing this application. This research has built upon our previously reported work [20, 21] on route optimizations and airspace sector design in an air traffic control network, by adding in the goals of interested entities, e.g. airlines, aircraft, and airports, maximizing the “payoff” to each player (agent). It is intended that the results of our work will be directly used in this domain. In addition, we envision our work being leveraged for other optimization tasks such as data traffic on a network, first responder / disaster relief efforts, and other tasks where rapid solving of large-scale optimization problems is essential.


Multiagent System Local Goal English Auction Intelligent Physical Agent Aircraft Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Castelli
    • 1
  • Joshua Lee
    • 1
  • Waseem Naqvi
    • 1
  1. 1.Raytheon Company, Network Centric SystemsMarlboroughUSA

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