Agile Control System for Reconfigurable Manufacturing Systems

生产系统的配置影响生产系统的性能，对生产系统的配置进行了大量的研究，如装配线平衡、单元形成等。在指定的配置下，生产系统的性能受生产排序和缓冲区分配的影响。对于分配了缓冲区的多阶段生产库存系统，订货系统确定每个阶段的订单发放时间和数量，而准时制订货系统根据实际需求确定订单发放的时间和数量。在以往关于JIT订货系统的大多数文献中，都假设了需求的稳定变化，而从来没有分析过需求的不稳定变化的影响。基于这样的背景，一些反应式系统被提出用于JIT订货系统。在反应式JIT订购系统中，检测需求的不稳定变化，并且作为对检测到的变化的反应来调整缓冲器大小。然而，仅通过调整缓冲区大小，其适应性是有限的。最近，可重构制造系统(RMS)被提出，并有可能实现根据环境变化调整配置的系统。因此，本报告介绍了在JIT订货系统控制的RMS中的动态生产线平衡和缓冲区分配。在该系统中，通过指数加权移动平均(EWMA)图检测需求的不稳定变化，并根据检测到的不稳定变化来改变生产系统的配置，即阶段数和缓冲区大小，并通过生产线平衡为每个阶段分配工作要素。此外，还应用神经网络来提高检测需求不稳定变化的性能。通过仿真实验对该系统的有效性进行了分析，并与以前的系统进行了比较。

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.