Optimizing usage of recycled material in a remanufacturing environment
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Remanufacturing has acquired importance in recent times because of increasing environmental concerns of manufacturing processes that deplete resources of the earth. Some examples of remanufactured products are automobile parts, furniture, photo-copiers, computer printers, etc. The standard remanufacturing set-up consists of assembling products from two sources (i) "cores" which are obtained from recycled products and (ii) "non-recycled" materials, also called unused materials, that are produced from minerals freshly mined from the earth. An important decision for the manager is to select material optimally from these two sources. Use of cores reduces manufacturing costs but generally increases the production time because of the additional pre-processing that is usually necessary. Increased production time can lead to an increase in the manufacturing lead time, which in all probability has a negative impact on the service levels. On the other hand, use of cores has a significant environmental benefit. 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 algorithm produced encouraging results.