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      제조 셀에서 Family-based dispatching을 바탕으로 한 Preemption에 관한 연구 = Family-based Dispatching with Preemption in Manufacturing Cells

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      https://www.riss.kr/link?id=T14460675

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      Family-based dispatching is a way to improve lead time performance at any work place. Exhaustive rules of family-based dispatching can avoid the setup time in order to promote the flowtime and tardiness performance. Although non-exhaustive rules add the number of setup, it can provide a flexibility and competitiveness when setup time is small. In this paper, we studied the preemption based on the family-based dispatching, which includes three purposes. The first one is to analyze the results after increasing the number of non-exhaustive rules and job-preemption. The second one is to get the optimal group by combining FBD rules with non-preemption, family-preemption and job-preemption one by one. The final one is to make clear of the factors affecting preemption and FBD.
      We adopted the Arena software package to simulate the environments of one machine and two machines, respectively. We found that one specific group can’t achieve the best performance for all cases. Conversely, it will change with the utilization ratio, set-up to runtime ratio and priority location.
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      Family-based dispatching is a way to improve lead time performance at any work place. Exhaustive rules of family-based dispatching can avoid the setup time in order to promote the flowtime and tardiness performance. Although non-exhaustive rules add t...

      Family-based dispatching is a way to improve lead time performance at any work place. Exhaustive rules of family-based dispatching can avoid the setup time in order to promote the flowtime and tardiness performance. Although non-exhaustive rules add the number of setup, it can provide a flexibility and competitiveness when setup time is small. In this paper, we studied the preemption based on the family-based dispatching, which includes three purposes. The first one is to analyze the results after increasing the number of non-exhaustive rules and job-preemption. The second one is to get the optimal group by combining FBD rules with non-preemption, family-preemption and job-preemption one by one. The final one is to make clear of the factors affecting preemption and FBD.
      We adopted the Arena software package to simulate the environments of one machine and two machines, respectively. We found that one specific group can’t achieve the best performance for all cases. Conversely, it will change with the utilization ratio, set-up to runtime ratio and priority location.

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      목차 (Table of Contents)

      • 제1장 서론 1
      • 1.1. 연구 배경 1
      • 1.2. 연구 내용 및 범위 2
      • 제2장 선행연구 3
      • 2.1. FBD에 대한 샵 구성 3
      • 제1장 서론 1
      • 1.1. 연구 배경 1
      • 1.2. 연구 내용 및 범위 2
      • 제2장 선행연구 3
      • 2.1. FBD에 대한 샵 구성 3
      • 2.2. FBD에 관련한 룰 3
      • 2.2.1 Decisions framework 3
      • 2.2.2 Exhaustive rules 4
      • 2.2.3 Non-exhaustive rules 5
      • 2.2.4 Preemption 6
      • 제3장 샵 소개와 의사결정 구조 7
      • 3.1. 샵 소개 7
      • 3.2. 의사결정 구조 8
      • 3.2.1 성과측정기준 8
      • 3.2.2 Switching moment 9
      • 3.2.3 Family selection 10
      • 3.2.4 Job selection 13
      • 제4장 실험 설계 및 분석 14
      • 4.1. Single machine 실험 14
      • 4.1.1 실험설계 14
      • 4.1.2 결과 분석 18
      • 4.2. Two machines 실험 55
      • 4.2.1 실험설계 55
      • 4.2.2 실험요인설정 55
      • 4.2.3 결과 분석 58
      • 제5장 결론 95
      • 참고문헌 99
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