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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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A Novel Approach to the Job Shop Scheduling Problem Based on the Deep Q-Network in a Cooperative Multi-Access Edge

Junhyung Moon1, Minyeol Yang1, Jongpil Jeong1

  • 1Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea.

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|July 20, 2021
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Summary

This study introduces a novel approach to manufacturing job shop scheduling using multi-access edge computing (MEC) and deep Q-network (DQN) reinforcement learning. The method enables efficient, secure, and low-latency scheduling without relying on cloud centers.

Keywords:
cooperative scheduling systemdeep Q-networkjob shop scheduling problemmanufacturing processmulti-access edge computing

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Area of Science:

  • Computer Science
  • Operations Research
  • Manufacturing Engineering

Background:

  • Cloud computing presents security and latency challenges for manufacturing processes.
  • Multi-access Edge Computing (MEC) offers a decentralized alternative for real-time applications.
  • Job shop scheduling is a complex optimization problem critical to manufacturing efficiency.

Purpose of the Study:

  • To propose a novel job shop scheduling method using MEC.
  • To address the limitations of cloud-based scheduling in manufacturing.
  • To leverage reinforcement learning for efficient edge-based scheduling.

Main Methods:

  • Applied deep Q-network (DQN), a reinforcement learning model, to solve the job shop scheduling problem.
  • Utilized transfer learning data to create an efficient DQN, mitigating computational overload.
  • Implemented cooperative scheduling between edge devices within an MEC framework, avoiding cloud dependency.

Main Results:

  • The proposed MEC-based framework successfully performed independent scheduling at the network edge.
  • The efficient DQN model demonstrated effective resource management and scheduling capabilities.
  • Evaluations showed competitive or improved performance compared to existing frameworks.

Conclusions:

  • MEC provides a viable and efficient alternative to cloud computing for manufacturing job shop scheduling.
  • Reinforcement learning, specifically DQN, is a powerful tool for optimizing edge-based manufacturing processes.
  • The proposed cooperative scheduling method enhances manufacturing flexibility and reduces operational risks.