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

  • 제24권3호
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  • Pages.99-110
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  • 2019
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  • 2288-3908(pISSN)
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  • 2765-3846(eISSN)
한국전자거래학회 (Society for e-Business Studies)
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납기와 작업준비비용을 고려한 병렬기계에서 딥러닝 기반의 일정계획 생성 모델

Scheduling Generation Model on Parallel Machines with Due Date and Setup Cost Based on Deep Learning

  • Yoo, Woosik (Department of Industrial and Management Engineering, Incheon National University) ;
  • Seo, Juhyeok (Department of Industrial and Management Engineering, Incheon National University) ;
  • Lee, Donghoon (Department of Industrial and Management Engineering, Incheon National University) ;
  • Kim, Dahee (Department of Industrial and Management Engineering, Incheon National University) ;
  • Kim, Kwanho (Department of Industrial and Management Engineering, Incheon National University)
  • 투고 : 2019.05.30
  • 심사 : 2019.07.25
  • 발행 : 2019.08.31
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초록

4차 산업혁명이 진행되면서 제조업에서 사물인터넷(IoT), 머신러닝과 같은 지능정보기술을 적용하는 사례가 증가하고 있다. 반도체/LCD/타이어 제조공정에서는 납기일(due date)을 준수하면서 작업물 종류 변경(Job change)으로 인한 작업 준비 비용(Setup Cost)을 최소화하는 일정계획을 수립하는 것이 효과적인 제품 생산을 위해 매우 중요하다. 따라서 본 연구에서는 병렬기계에서 딥러닝 기반의 납기 지연과 작업 준비 비용 최소화를 달성하는 일정계획 생성 모델을 제안한다. 제안한 모델은 과거의 많은 데이터를 이용하여 고려되어지는 주문에 대해 작업 준비와 납기 지연을 최소화하는 패턴을 학습한다. 따라서 세 가지 주문 리스트의 난이도에 따른 실험 결과, 본 연구에서 제안한 기법이 기존의 우선순위 규칙보다 성능이 우수하다는 것을 확인하였다.

As the 4th industrial revolution progressing, manufacturers are trying to apply intelligent information technologies such as IoT(internet of things) and machine learning. In the semiconductor/LCD/tire manufacturing process, schedule plan that minimizes setup change and due date violation is very important in order to ensure efficient production. Therefore, in this paper, we suggest the deep learning based scheduling generation model minimizes setup change and due date violation in parallel machines. The proposed model learns patterns of minimizing setup change and due date violation depending on considered order using the amount of historical data. Therefore, the experiment results using three dataset depending on levels of the order list, the proposed model outperforms compared to priority rules.

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참고문헌

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