| Co-Investigator(Kenkyū-buntansha) |
MORIKAWA Katsumi Hiroshima University, Graduate School of Engineering, Associate Professor, 大学院・工学研究科, 助教授 (10200396)
KAMIGAKI Tamotsu Hiroshima Kokusai Gakuin University, School of Engineering, Lecturer, 工学部, 講師 (80261064)
MYRESHKA 広島大学, 大学院・工学研究科, 助手 (40346523)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2005: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2004: ¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2003: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Research Abstract |
The configuration of production system affects the performance of the production system, and there is a lot of research on configuring production systems, such as assembly line balancing and cell formation. Under the specified configuration, the performance of production system is affected by production ordering and buffer allocation. Ordering systems determine the timing and quantity in order release for each stage of multi-stage production inventory systems with allocated buffer, and just-in-time (JIT) ordering systems determine them on the basis of the actual demand. In most of the previous literature on JIT ordering systems, stable changes in demand have been assumed, and the influence of unstable changes in demand had never been analyzed. Based on the background, some reactive systems have been proposed for the JIT ordering system. In the reactive JIT ordering system, unstable changes in demand are detected, and the buffer size is adjusted as a reaction to the detected changes. However, only by adjusting the buffer size, the adaptability is limited. Recently, reconfigurable manufacturing systems (RMSs) have been pointed out, and systems which can adjust the configuration in response to environmental changes are likely to be realized. Therefore, this report introduces dynamic line balancing and buffer allocation in a RMS controlled by the JIT ordering system. In the proposed system, unstable changes in demand are detected by the exponentially weighted moving average (EWMA) charts, and the configuration of production system, that is, the number of stages and buffer size, are altered in response to the detected unstable changes, and work elements are allocated to each stage by line balancing. Also, a neural network is applied to improve the performance to detect unstable changes in demand. The effectiveness of the proposed system is analyzed and compared with that of the previous system by simulation experiments.
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