• 제목/요약/키워드: Critical WIP Loops

검색결과 3건 처리시간 0.016초

생산환경 변화에 따른 최적 Material Flow Control 선택방법 (Selection Method for Optimal Shop Floor Control According to Manufacturing Environment)

  • 박상근;박성호;하정훈
    • 산업경영시스템학회지
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    • 제36권2호
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    • pp.81-90
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    • 2013
  • Material flow control (MFC) is a kind of operational policy to control of the movement of raw materials, components, and products through the manufacturing lines. It is very important because it varies throughput, line cycle time, and work-in-process (WIP) under the same manufacturing environments. MFC can be largely categorized into three types such as Push, Pull, and Hybrid. In this paper, we set various manufacturing environments to compare five existing MFC mechanisms: Push, Pull, and Hybrid (CONWIP, Gated MaxWIP, Critical WIP Loops, etc). Three manufacturing environments, manufacturing policies (make to stock and make to order), demand (low, medium, high), and line balancing (balanced, unbalanced, and highly unbalanced) are considered. The MFCs are compared in the point of the five functional efficiencies and the proposed compounded efficiency. The simulation results shows that the Push is superior in the functional efficiency and GMWIP is superior in the compounded efficiency.

시뮬레이션을 통한 생산흐름통제시스템의 성능비교 (Performance Comparison between Material Flow Control Mechanisms Using Simulation)

  • 박상근;하정훈
    • 산업경영시스템학회지
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    • 제35권1호
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    • pp.115-123
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    • 2012
  • Material flow control mechanism is a kind of operational policy in manufacturing. It is very important because it varies throughput, throughput time, and work-in-process (WIP) under the same manufacturing resources. Many Researchers have developed various material flow control mechanisms and insisted that their mechanism is superior to others. However the experimental environment used in the performance comparison are different and impractical. In this paper, we set various manufacturing environments to fairly compare five previous material flow control mechanism : Push, Pull, CONWIP, Gated MaxWIP, and Critical WIP Loops. The simulation results show that the Push is superior to others in both of throughput and WIP if required demand is less than 80% of capacity. In addition, the performance of CONWIP and its variants are not different statistically.

유전 알고리즘을 이용한 Work-In-Process 수준 최적화 (Optimizing Work-In-Process Parameter using Genetic Algorithm)

  • 김정섭;정지용;이종환
    • 산업경영시스템학회지
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    • 제40권1호
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    • pp.79-86
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    • 2017
  • This research focused on deciding optimal manufacturing WIP (Work-In-Process) limit for a small production system. Reducing WIP leads to stable capacity, better manufacturing flow and decrease inventory. WIP is the one of the important issue, since it can affect manufacturing area, like productivity and line efficiency and bottlenecks in manufacturing process. Several approaches implemented in this research. First, two strategies applied to decide WIP limit. One is roulette wheel selection and the other one is elite strategy. Second, for each strategy, JIT (Just In Time), CONWIP (Constant WIP), Gated Max WIP System and CWIPL (Critical WIP Loops) system applied to find a best material flow mechanism. Therefore, pull control system is preferred to control production line efficiently. In the production line, the WIP limit has been decided based on mathematical models or expert's decision. However, due to the complexity of the process or increase of the variables, it is difficult to obtain optimal WIP limit. To obtain an optimal WIP limit, GA applied in each material control system. When evaluating the performance of the result, fitness function is used by reflecting WIP parameter. Elite strategy showed better performance than roulette wheel selection when evaluating fitness value. Elite strategy reach to the optimal WIP limit faster than roulette wheel selection and generation time is short. For this reason, this study proposes a fast and reliable method for determining the WIP level by applying genetic algorithm to pull system based production process. This research showed that this method could be applied to a more complex production system.