Abstract
In this study, based on multi-access edge computing (MEC), we provided the possibility of cooperating manufacturing processes. We tried to solve the job shop scheduling problem by applying DQN (deep Q-network), a reinforcement learning model, to this method. Here, to alleviate the overload of computing resources, an efficient DQN was used for the experiments using transfer learning data. Additionally, we conducted scheduling studies in the edge computing ecosystem of our manufacturing processes without the help of cloud centers. Cloud computing, an environment in which scheduling processing is performed, has issues sensitive to the manufacturing process in general, such as security issues and communication delay time, and research is being conducted in various fields, such as the introduction of an edge computing system that can replace them. We proposed a method of independently performing scheduling at the edge of the network through cooperative scheduling between edge devices within a multi-access edge computing structure. The proposed framework was evaluated, analyzed, and compared with existing frameworks in terms of providing solutions and services.
Highlights
Published: 2 July 2021Owing to the smartly changing development of the manufacturing process, the problem of efficiently handling more complex scheduling problems within the network of complex industrial sites with diversified manufacturing equipment has emerged
In multi-access edge computing (MEC), we proposed an architecture that optimizes the learning speed and resource consumption by applying transfer learning to the deep-Q network (DQN)
In ETSI, MEC changed its name to ess edge computing instead of mobile edge computing to apply it to various areas beyond WiFi and mobile fixed-access technology [34]
Summary
Owing to the smartly changing development of the manufacturing process, the problem of efficiently handling more complex scheduling problems within the network of complex industrial sites with diversified manufacturing equipment has emerged. This paper proposed an architecture that performs all scheduling tasks at the edge of the network with DQN, playing the role of network monitoring and security, as well as orchestration and computing resource management at the cloud center [12]. An attempt was made to solve the scheduling problem using DQN in an edge-computing-based smart factory framework, there may be limitations in terms of performance to handle all tasks in an edge device. In order to determine the dispatch rules for all edge devices in the MEC-based cooperative edge computing framework, this white paper adapted the DQN to make multiple decisions to suit the requirements of the problem in question; We conducted comparative studies on cloud computing, a cloud–edge hybrid, and MEC.
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