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低碳异质分布式灵活工作商店中的动态智能调度,具有工作插入和转移
Yi Chen1,2, Xiaojuan Liao1,2, Guangzhu Chen1,2
1College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究通过开发一种新的深度强化学习方法来解决低碳异质分布式灵活工作室安排问题 (LHDFJSP). 彩虹DQN方法有效地减少了动态智能制造环境中的延迟和能源消耗.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 制造系统工程 制造系统工程
背景情况:
- 经济全球化和绿色制造推动了工作室调度的演变.
- 智能制造面临着动态事件和复杂的调度挑战.
- 现有的研究不足地解决了LHDFJSP因素,如异质性,动态插入和低碳目标.
研究的目的:
- 为LHDFJSP建立一个多目标的数学模型.
- 为了最大限度地减少总加权延误和总能耗.
- 为动态的制造环境制定适应性调度策略.
主要方法:
- 为LHDFJSP制定一个多目标数学模型.
- 开发多种复合编程规则.
- 彩虹深度Q网络 (彩虹DQN) 的应用,用于动态调度策略学习.
主要成果:
- 提出的基于彩虹DQN的方法在解决LHDFJSP方面表现出有效性.
- 对扩展数据集的评估证实了该方法的概括能力.
- 该方法在处理动态事件和复杂的调度因素方面被证明是可靠的.
结论:
- 彩虹DQN框架为LHDFJSP提供了有效的解决方案.
- 该方法成功地平衡了尽量减少延迟和能源消耗.
- 该方法适用于需要适应性调度的动态智能制造环境.
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