4.7 Article

Inventory management for a remanufacture-to-order production with multi-components (parts)

期刊

JOURNAL OF INTELLIGENT MANUFACTURING
卷 30, 期 1, 页码 59-78

出版社

SPRINGER
DOI: 10.1007/s10845-016-1232-z

关键词

Remanufacturing; Original equipment manufacturing; Multiple varieties; Multi-level inventory; Inventory management

资金

  1. MOST (the Ministry of Science and Technology of China) [2013AA040206, 2012BAF12B20, 2012BAH08F04]
  2. National Natural Science Foundation of China [51035001, 51121002, 71131004]
  3. National High Technology Research and Development Program of the P.R. China [2013AA040206]

向作者/读者索取更多资源

In remanufacturing industries, inventory is significant because remanufacturing industries includes the stock of recycled products, stock of components (parts) after disassembly and refurbishing operation, stock of new parts purchased from manufacturers and stock of remanufactured products. Therefore, current research presents an approach to search an appropriate inventory policy for single product with multi-components for a third party remanufacturing company cooperated with the original equipment manufacturer (OEM). The third party for remanufacturing working in close cooperation with the OEM (such as dealer) can acquire the regional protection rules and can gain the technical support from the OEM. A general mathematical model is developed to describe the multiple components and multi-level inventory. Moreover, a case company problem is solved to validate the proposed model. Base on considered problem, the proposed model provided an efficient inventory management method to solve the situation in which remanufacturing company only recycled products but not components (parts). Furthermore, the presented model is significant to help the case company to set the best resource allocation by analyzing the impact on inventory policy from different replenishment quantities in remanufacturing process (recycling, disassembling and refurbishing). At last the proposed model is proved by a numerical experiment which used genetic algorithm (GA) to solve the inventory policy, and the result discusses the sensitivity analysis for model parameters in different replenishment quantities scenarios.

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