Smart recovery decision-making for end-of-life products in the context of ubiquitous information and computational intelligence

Smart product recovery decision-making (SRDM) plays a critical role in the closed-loop manufacturing chain. SRDM can facilitate the maximum reclamation of End-of-Life product value while achieving sustainability. Ubiquitous information and computational intelligence technologies empower the implementation of SRDM. These emerging smart technologies not only reduce the information uncertainties but also provide powerful operational methodologies for SRDM. However, there is a lack of a systematic and comprehensive framework to assist practitioners in better understanding SRDM in both theory and practice, as well as directing how to apply these emerging techniques to facilitate SRDM at the operational level. This study is the first effort to address this gap by providing a state-of-art review of SRDM techniques. First, the paper discusses the SRDM enablers and highlights their contributions. The SRDM enablers include the information technique, smart equipment, service platform and closed-loop supply chains. Then the SRDM techniques are reviewed in four aspects: model input prediction, recovery option determination, process planning, and production scheduling and planning. Further, a generalized SRDM implementation framework with a methodology roadmap is proposed to assist practitioners in applying the framework to practice. Finally, the challenges and opportunities in SRDM, inherited, derived, and enabled by the ubiquitous information and computational intelligence technologies, are identified and discussed. This study shows that the research on SRDM remains at an early stage. With the roadmap and insights provided in this work, more research can potentially advance SRDM. (C) 2020 Elsevier Ltd. All rights reserved.