Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process
This work addresses operational efficiency and maintenance scheduling for industrial manufacturing systems, presenting an incremental improvement by integrating task assignment and health management with MDPs.
The paper tackles the problem of manufacturing task assignment and machine health maintenance by proposing a distributed decision-making approach using Markov decision processes, which incorporates uncertainty and is demonstrated through a numerical case study with milling machine tool degradation data, showing flexibility in cost parameter selection and enabling offline policy analysis.
This paper considers a distributed decision-making approach for manufacturing task assignment and condition-based machine health maintenance. Our approach considers information sharing between the task assignment and health management decision-making agents. We propose the design of the decision-making agents based on Markov decision processes. The key advantage of using a Markov decision process-based approach is the incorporation of uncertainty involved in the decision-making process. The paper provides detailed mathematical models along with the associated practical execution strategy. In order to demonstrate the effectiveness and practical applicability of our proposed approach, we have included a detailed numerical case study that is based on open source milling machine tool degradation data. Our case study indicates that the proposed approach offers flexibility in terms of the selection of cost parameters and it allows for offline computation and analysis of the decision-making policy. These features create and opportunity for the future work on learning of the cost parameters associated with our proposed model using artificial intelligence.