多智能体制造中的任务重调度

M. Fletcher, S. Deen
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引用次数: 15

摘要

我们提出了一个多智能体制造中的任务重调度模型,该模型旨在在具有可预测故障模式的环境中最大化系统效率和可靠性。我们描述了一个分布式制造系统架构(http://hms.ifw.uni-hannover.de/public/overview.html)来说明重新调度的优点。我们还开发了一个基于资源冷却的动作再分配框架,即将动作从使用率最高或故障(最热)代理的资源迁移到最冷(约束最少)的资源。此外,本文还讨论了在执行分散生产任务的并行资源上重新调度级联时资源冷却的成本与收益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Task rescheduling in multi-agent manufacturing
We present a model for task rescheduling in multi-agent manufacturing that is geared toward maximising system efficiency and reliability in an environment with predictable failure patterns. We describe a distributed manufacturing system architecture (http://hms.ifw.uni-hannover.de/public/overview.html) to illustrate the merits of rescheduling. We also develop an action redistribution framework based upon resource cooling, i.e. migrating actions from the most utilised or faulty (hottest) agents' resources to the coldest (least constrained) ones. Furthermore, the paper discusses the costs versus benefits of resource cooling when rescheduling cascades over parallel resources executing a decentralised production task.
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