Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912661170552832 |
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| author | Nasir, Ali Mekid, Samir Sawlan, Zaid Alsawafy, Omar |
| author_facet | Nasir, Ali Mekid, Samir Sawlan, Zaid Alsawafy, Omar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_00042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process Nasir, Ali Mekid, Samir Sawlan, Zaid Alsawafy, Omar Artificial Intelligence Systems and Control 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. |
| title | Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2402.00042 |