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 that this approach leads to results close to the optimal ones, as the developed ML models are able to estimate the optimal policies for most of the parts.