The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling
Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of produ...
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| Format: | Article |
| Language: | English |
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Ayandegan Institute of Higher Education,
2024-03-01
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| Series: | International Journal of Research in Industrial Engineering |
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| Online Access: | https://www.riejournal.com/article_186497_605940651dc6891c9c467919ff18f1c2.pdf |
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| author | Mohammad Reza Nazabadi Seyed Najafi Ali Mohaghar Farzad Movahedi Sobhani |
| author_facet | Mohammad Reza Nazabadi Seyed Najafi Ali Mohaghar Farzad Movahedi Sobhani |
| author_sort | Mohammad Reza Nazabadi |
| collection | DOAJ |
| description | Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems. |
| format | Article |
| id | doaj-art-f2083071ca05433bbc878d554dd24168 |
| institution | Kabale University |
| issn | 2783-1337 2717-2937 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj-art-f2083071ca05433bbc878d554dd241682025-01-30T15:10:09ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372024-03-01131718710.22105/riej.2023.298557.1240186497The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modelingMohammad Reza Nazabadi0Seyed Najafi1Ali Mohaghar2Farzad Movahedi Sobhani3Department of Industrial Engineering, Science and Research Branch, Islamic Azad University Tehran, Iran.Department of Industrial Engineering, Science and Research Branch, Islamic Azad University Tehran, Iran.Faculty of Industrial Management, University of Tehran, Tehran, Iran.Department of Industrial Engineering, Science and Research Branch, Islamic Azad University Tehran, Iran.Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems.https://www.riejournal.com/article_186497_605940651dc6891c9c467919ff18f1c2.pdfagent-based modelingreinforcement learningsimulation-optimizationproduction planningmaintenancequality control |
| spellingShingle | Mohammad Reza Nazabadi Seyed Najafi Ali Mohaghar Farzad Movahedi Sobhani The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling International Journal of Research in Industrial Engineering agent-based modeling reinforcement learning simulation-optimization production planning maintenance quality control |
| title | The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling |
| title_full | The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling |
| title_fullStr | The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling |
| title_full_unstemmed | The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling |
| title_short | The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling |
| title_sort | joint policy of production maintenance and product quality in a multi machine production system by reinforcement learning and agent based modeling |
| topic | agent-based modeling reinforcement learning simulation-optimization production planning maintenance quality control |
| url | https://www.riejournal.com/article_186497_605940651dc6891c9c467919ff18f1c2.pdf |
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