Selecting Part Feeding Policies with a Combined Optimization-Machine Learning Approach

In several industries, increasing attention is being devoted to the design and management of part feeding systems. This paper applies a combined optimization-machine learning (ML) approach for part feeding policies selection to the case of a truck assembly plant. According to this approach, feeding policies are selected through a ML model, trained using the output of an optimization model previously applied to a sample of parts. Results show...