Remanufacturing with random yield in the presence of the take-back regulation. (June 2022)

• Record Type: Journal Article
• Authors: Xu, Jie; Luo, Chunlin; Ng, C.T.; Feng, Xuehao; Cheng, T.C.E.
• Is Part Of: Computers & industrial engineering. Volume 168(2022)
• Publication Date: 2022-06
• Subjects: Manufacturing -- Remanufacturing -- End-of-use products -- Take-back regulation -- Random yield; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108097
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Consider both new products and remanufactured products co-exist in the market. Develop a model to investigate the OEM's (re) manufacturing and recycling decisions. With random remanufacturing yield rate, derive the analytical solutions for the OEM. Derive three different decision regions depending on the inverse operating cost. Conduct real data numerical study, show the applicability and robustness of our model. Abstract: The amount of e-waste all over the world has reached an astounding quantity and is still growing steadily. To address the issue, an increasing number of governments have implemented the take-back regulation, which requires the original equipment manufacturer (OEM) to be responsible for collecting and remanufacturing its end-of-use products. Incorporating the randomness of the remanufacturing yield rate, we develop a model to investigate the OEM's (re) manufacturing and recycling decisions when new products and remanufactured products co-exist in the market. Using backward induction, we derive the analytical solutions for the OEM, finding that there are three different decision regions depending on the inverse operating cost. Moreover, there exists a threshold for the inverse operating cost, below which the OEM's collection behaviour is voluntary while above which the OEM is forced to reach the requirements of the regulation. In addition, we conduct numerical studies using Apple's cell-phone data and find that our analytical findings agree with the real outcomes, which confirms the applicability and robustness of our model.