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首页> 外文会议>IFIP 159; IFIP TC 5 / WG 5.5 IFIP International Conference on Information Technology for Balanced Automation Systems in Manufacturing And Services; 20040927-29; Vienna(AT) >OPTIMIZATION ALGORITHM FOR DYNAMIC MULTI-AGENT JOB ROUTING
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OPTIMIZATION ALGORITHM FOR DYNAMIC MULTI-AGENT JOB ROUTING

机译：动态多Agent作业路由的优化算法

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摘要

Current research in multi-agent heterarchical control for holonic systems is usually focused in real-time scheduling algorithms, where agents explore the routing or process sequencing flexibility in real-time. In this paper we investigate the impact of the dynamic job routing and job sequencing decisions on the overall optimization of the system's performance. An approach to the optimization of local decisions to assure global optimization is developed within the framework of a Neural Collective Intelligence (NECOIN). Reinforcement learning (RL) algorithms are used at the local level, while generalization of Q-neural algorithm is used to optimize the global behaviour. A simulation test bed for the evaluation of such types of multi-agent control architectures for holonic manufacturing systems integrating discrete-event simulation facilities is implemented over JADE agent platform. Performance results of the simulation experiments are presented and discussed.
机译：整个系统的多代理程序分层控制的当前研究通常集中在实时调度算法上，在该算法中，代理程序实时探索路由或流程排序的灵活性。在本文中，我们研究了动态作业路由和作业排序决策对系统性能的整体优化的影响。在神经集体智能（NECOIN）的框架内，开发了一种用于确保全局优化的局部决策优化方法。强化学习（RL）算法用于局部级别，而Q神经算法的推广则用于优化全局行为。在JADE Agent平台上实现了一个仿真测试台，用于评估集成了离散事件仿真设施的整体制造系统的此类多Agent控制体系结构。提出并讨论了仿真实验的性能结果。

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