Optimal sorting policies in remanufacturing systems: Application of product life-cycle data in quality grading and end-of-use recovery

The quality of used products returned to recovery facilities is often highly uncertain. Quality grading and sorting policies are immediate solutions that are used in remanufacturing systems to handle this source of variability in incoming products. The sorting policies offered in the literature so far are mainly based on external criteria such as market trends, corporate policies and assessment of the product's physical condition. In this study, we offer a new sorting method based on both product's internal factors such as future reusability of components, product identity data, and product health status as well as external factors such as market trends. The purpose of this paper is to improve decision making in remanufacturing operations by integrating the product life cycle information, particularly product usage phase data, into determining both optimal sorting policies and End-of-Life/End-of-Use (EoL/EoU) decisions. To achieve this, two related analyses are conducted: first, a reusability index is derived for each product unit based on the reusability of its components, product features, and the product usage phase information. Second, the reusability index is used as a quality measure to derive the optimal EoU decision for each product category. Clustering algorithms are employed to identify similar products that could go through the same recovery process. A data set of hard disk drive Self-Monitoring Analysis and Reporting Technology (S.M.A.R.T.) coupled with simulated data has been analyzed to illustrate the application of the model. The proposed framework helps decision makers include product identity data in the EoU recovery decision making under the quality heterogeneity.