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. 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 study 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.
