Optimization of Automotive Reverse Supply Chain (RSC) based on AI Techniques


The rise of circular economy and the development of Industry 4.0 urge more scholars to focus on the field of end-of-life vehicles (ELVs) management. Due to the uncertainty of recovery quantities and the lack of transparency of information, the operation efficiency of the automobile reverse logistics network (RLN) is low at present. The aim of my research is to improve automotive RSC in the context of sustainable development by applying AI techniques to facilitate efficient logistics and information flow of automotive RSC under uncertainty. Reverse logistics and information flow supported by AI can optimize the whole automative RSC, thus improving the utilization rate of enterprise resources, reducing costs and solving the problem of low operational efficiency.


Hanbing Xia is a PhD student specializing in supply chain technology in the age of Industry 4.0. Before joining the System Realization Lab at the University of Liverpool in the United Kingdom, she earned a bachelor’s degree in Logistics Management in 2017 and a Master’s degree in Supply Chain Management in 2019 from Jilin University, China. Her research focus is on optimizing reverse supply chain (RSC) using artificial intelligent (AI) techniques.