Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops
Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops
复制标题
基于流形学习的 RFID 驱动作业车间隐性干扰的重新调度决策机制
DOI：
10.1007/s10845-016-1194-1
复制
发表时间：
2016-01
影响因子：
8.3
通讯作者：
Pingyu Jiang
中科院分区：
文献类型：
--
作者：
Chuang Wang;Pingyu Jiang
在实际的制造过程中，一些意想不到的干扰，称为隐性干扰（例如，作业准备时间变化和到达时间偏差）会逐渐使原来的生产计划变得过时。生产经理很难察觉到他们的存在。因此，隐性干扰的影响并不能通过及时重新安排来消除。鉴于此，本文提出了一种针对RFID驱动作业车间隐性干扰的重新调度决策机制，并在该机制中应用了流形学习方法来预处理制造数据，从而减少了制造系统的响应时间。重新安排决策机制有望回答是否重新安排、何时重新安排以及采用哪种重新安排方法的问题。首先，RFID设备在每个WIP加工过程完成时间获取所有在制品(WIP)的实际过程完成时间。其次，隐性干扰被量化为时间累积误差（TAE），它代表实际过程完成时间与计划过程完成时间之间的差异。最后，根据TAE和生产经理的经验，重新调度决策机制选择合适的重新调度方法来更新或修复原始生产计划。重调度决策机制的实现算法包括： (1)有监督局部线性嵌入。 (2)一般回归神经网络。 (3)最小二乘支持向量机。最后通过数值实验验证了重调度决策机制的实现过程。
In actual manufacturing processes, some unexpected disturbances, called as recessive disturbances (e.g., job set-up time variation and arrival time deviation), would gradually make the original production schedule obsolete. It is hard for production managers to perceive their presences. Thus, the impact of recessive disturbances can not be eliminated by rescheduling in time. On account of this, a rescheduling decision mechanism for recessive disturbances in RFID-driven job shops is proposed in this article, and a manifold learning method, which reduces the response time of manufacturing system, is applied in the mechanism to preprocess manufacturing data. The rescheduling decision mechanism is expected to answer the questions of whether to reschedule, when to reschedule, and which rescheduling method to be used. Firstly, RFID devices acquire the actual process completion time of all work in process (WIPs) at every WIP machining process completion time. Secondly, recessive disturbances are quantified to time accumulation error (TAE) which represents the difference between actual process completion time and planned process completion time. Lastly, according to the TAE and production managers’ experience, the rescheduling decision mechanism selects a proper rescheduling method to update or repair the original production schedule. The realization algorithms of rescheduling decision mechanism includes: (1) supervised locally linear embedding. (2) General regression neural network. (3) Least square-support vector Machine. Finally, a numerical experiment is used to demonstrate the implementation procedures of the rescheduling decision mechanism.
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DOI：
10.1016/s0377-2217(97)00396-2
发表时间：
1999
期刊：
Eur. J. Oper. Res.
影响因子：
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作者：
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2000-03
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DOI：
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发表时间：
2004-06
期刊：
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影响因子：
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作者：
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影响因子：
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