한국전자거래학회지 (The Journal of Society for e-Business Studies)

  • 제25권1호
  • /
  • Pages.1-12
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  • 2020
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  • 2288-3908(pISSN)
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  • 2765-3846(eISSN)
한국전자거래학회 (Society for e-Business Studies)
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제조라인의 학습기반 디스패처를 위한 디스패치 의사결정 평가 시각화시스템

A Decision Monitoring System for Machine Learning Based Dispatcher of Manufacturing Lines

  • Huh, Jaeseok (Department of Business Administration, Korea Polytechnic University) ;
  • Park, Jonghun (Department of Industrial Engineering and Institute for Industrial Systems Innovation, Seoul National University)
  • 투고 : 2019.04.04
  • 심사 : 2020.02.11
  • 발행 : 2020.02.28
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초록

최근에 기계학습을 적용한 연구들이 다양한 분야에서 뛰어난 성과를 보임에 따라, 제조업 분야에서도 학습기반의 디스패처는 학계와 산업계의 관심의 대상이 되고 있다. 디스패처의 성능을 향상시키기 위해서는 각 디스패치 의사결정을 자세히 평가할 수 있어야 한다. 그러나 기존의 제조라인에 대한 시각화기법에 대한 연구들은 제조라인의 성능지표나 이상치를 시각화하는데 주로 집중되었다. 본 논문은 디스패치 의사결정이 수행되는 시점에 선택 가능한 대안들과 함께 제조라인에 대한 다양한 정보를 제공하는 시각화시스템을 제안한다. 또한, 제안된 시스템은 설비의 유휴시간의 원인을 효과적으로 나타내며, 시간에 따른 성능지표의 변화도 효과적으로 표현하였다.

Recently, research using machine learning have shown remarkable results in various domains, leading to the fact that leaning-based dispatchers have intrigued interest in both academia and industry. To improve the performance of the dispatcher, each dispatch decision needs to be evaluated in detail. However, existing studies on visualization techniques for manufacturing lines have mainly focused on illustrating the performance indicators or abnormal patterns. In this paper, we propose a monitoring system that displays a variety of information about the manufacturing line along with alternatives at the time of each dispatching decision being made. Furthermore, the proposed system effectively represents the cause of the idle time of resources and the change of the performance index over time.

키워드

참고문헌

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