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首页> 外文会议>Database and Expert Systems Applications; Lecture Notes in Computer Science; 4080 >An Applied Optimization Framework for Distributed Air Transportation Environments
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An Applied Optimization Framework for Distributed Air Transportation Environments

机译：分布式航空运输环境的应用优化框架

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摘要

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 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.
机译：在具有多个分布式实体的大型动态系统中，每个实体都有自己的兴趣点，需要找到一种全局可接受的最佳解决方案状态。根据定义，此解决方案状态对于所有实体而言都相对于其各自的目标和整个系统而言都是有效的。在这些动态环境中，可以通过利用博弈论领域的软件技术来实现此解决方案状态，以便做出最佳决策。我们提出了一个基于通用优化框架的应用程序，该框架可应用于多个领域，例如货物或网络流量算法。在这项研究中，我们通过建模和仿真环境使用了基于市场的空中交通流量管理方法。目的是允许单个飞机具有一定程度的本地自治权，就像高速公路上的汽车一样。我们的系统能够实时处理诸如节点丢失之类的故障，并相应地调整系统参数，以根据相关代理的目标优化结果。我们描述了不同代理交互框架之间在市场机制拍卖中的表现之间的权衡。我们还将讨论在实施此应用程序时获得的经验教训。这项研究的基础是我们先前报告的有关空中交通管制网络中航线优化和空域部门设计的工作，方法是增加感兴趣实体的目标，例如：航空公司，飞机和机场，从而最大程度地提高了对每个玩家（代理）的收益。我们的工作结果将直接用于此领域。此外，我们设想将我们的工作用于其他优化任务，例如网络上的数据流量，急救人员/救灾工作以及其他需要快速解决大规模优化问题的任务。

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