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首页> 外文期刊>International Journal of Production Research >A machine learning approach to optimise the usage of recycled material in a remanufacturing environment
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A machine learning approach to optimise the usage of recycled material in a remanufacturing environment

机译：一种机器学习方法，可在再制造环境中优化回收材料的使用

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摘要

Remanufacturing has acquired importance in recent years because of the increasing environmental concerns of manufacturing processes that deplete the Earth's resources. Some examples of remanufactured products are automobile parts, furniture, photocopiers, and computer printers. In a remanufacturing setup, raw materials are drawn from two sources: (ⅰ) 'cores', which are obtained from recycled products, and (ⅱ) 'non-recycled' or unused materials, which are produced from minerals freshly mined from the earth. An important decision for the manager is to select material optimally from these two sources. Using cores has environmental benefits, and because they are cheap, they reduce manufacturing costs. However, their use generally increases the production time, because of the additional pre-processing usually needed, which can negatively impact service levels. When the supply of finished products is running low, to satisfy service levels, it makes sense to use unused material. This research focuses on identifying an optimal strategy of switching between the two sources of material. A reinforcement learning algorithm is used to solve the switching problem. The switching algorithm produced encouraging results, showing up to 65% cost improvements over a policy that uses only unused materials.
机译：近年来，由于制造过程中越来越多的环境问题使地球资源枯竭，因此再制造变得越来越重要。再制造产品的一些示例是汽车零件，家具，复印机和计算机打印机。在再制造过程中，原材料是从两个来源中提取的：（ⅰ）从回收产品中获得的“芯”，以及（ⅱ）从地球上新鲜开采的矿物中产生的“非回收”或未使用的材料。 。对于管理者而言，重要的决定是从这两个来源中选择最佳的物料。使用磁芯具有环境效益，并且由于价格便宜，因此可以降低制造成本。但是，由于通常需要进行额外的预处理，因此使用它们通常会增加生产时间，这可能会对服务水平产生负面影响。当成品供应量不足时，为了满足服务水平，使用未使用的物料是有意义的。这项研究的重点是确定在两种材料之间切换的最佳策略。强化学习算法用于解决切换问题。切换算法产生了令人鼓舞的结果，与仅使用未使用材料的策略相比，其成本降​​低了多达65％。

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