Reinforcement Learning based Optimization of Bayesian Networks for Generating Feasible Vehicle Configuration Suggestions

A promising method in the automotive industry to anticipate future customer demands is the concept of planned orders. Due to multi-variant products, changing customer demands, and dynamic environments the process of generating planned orders is challenging. This paper introduces an approach using graphical models to generate planned order suggestions in a multi-variant order management process. Bayesian networks are modelled by learning the structure from different data sources, which enable the possibility to directly sample configuration suggestions. To find an optimized graph structure, a method using hierarchical correlation clustering and reinforcement learning is applied, taking into account technical and sales-operated feasibility constraints. The method has high potential in practical usage and is evaluated by a realworld use case of the Dr. Ing. h.c. F. Porsche AG.