Hybrid approach combining Bayesian network and rule-based systems for resource optimization in industrial cleaning processes

Probabilistic machine learning approaches has been successfully applied in various applications and is gaining more and more popularity. But the success of such approaches are based on the quality of the data. Getting quality data is the biggest challenge for most of the real-life applications and our application domain, i.e. industrial cleaning process, is no exception. In our application domain, the data collection is mostly performed manually without using any standards and is highly influenced by the expertise and interpretation of individual cleaning personnel. We have developed a Bayesain predictive assistance system (BPAS) that uses a real-life cleaning data to provide decision support to the cleaning personnel. In this paper, we extend our BPAS and propose a hybrid approach to develop an assistance system for resource optimization in industrial cleaning processes. The proposed approach, which combines Bayesian network and rule-based system, aims at increasing the robustness and the stability of the assistance system.