智能制造调度系统:基于协同边缘计算的DQN

Junhyung Moon, Jongpil Jeong
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引用次数: 6

摘要

本文采用深度q网络(Deep Q-Network, DQN)来解决智能工厂过程中的作业车间调度问题(Job shop Scheduling Problem, JSP)。另一方面,云计算在制造过程中存在通信延迟时间和安全问题等敏感问题。已经对引入边缘计算系统来取代它的各个方面进行了研究。在智能工厂边缘计算环境中,我们提出了一种多接入边缘计算(MEC)结构中边缘设备之间的协同调度,以便在没有云中心的帮助下进行调度。利用高效DQN进行迁移学习数据实验,并从智能工厂服务提供者的角度与现有框架进行对比分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Manufacturing Scheduling System: DQN based on Cooperative Edge Computing
In this paper, Deep Q-Network (DQN) was adopted to solve the Job shop Scheduling Problem (JSP) in the smart factory process. On the other hand, cloud computing has sensitive issues in the manufacturing process such as communication delay time and security problems. Research on various aspects of introducing an edge computing system to replace it has been conducted. We propose cooperative scheduling among edge devices in a Multi access Edge Computing (MEC) structure for scheduling without the help of a cloud center in a smart factory edge computing environment. Moreover, efficient DQN is used for experiments based on transfer learning data, and the proposed framework is compared and analyzed with existing frameworks from the perspective of provider a smart factory service.
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