A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment
Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9110908/ |
| _version_ | 1818579548734750720 |
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| author | Yeou-Ren Shiue Ken-Chuan Lee Chao-Ton Su |
| author_facet | Yeou-Ren Shiue Ken-Chuan Lee Chao-Ton Su |
| author_sort | Yeou-Ren Shiue |
| collection | DOAJ |
| description | Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism. |
| first_indexed | 2024-12-16T07:03:27Z |
| format | Article |
| id | doaj.art-120fb014b4dc4afc85f2866eccd1498f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T07:03:27Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-120fb014b4dc4afc85f2866eccd1498f2022-12-21T22:40:05ZengIEEEIEEE Access2169-35362020-01-01810654210655310.1109/ACCESS.2020.30007819110908A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility EnvironmentYeou-Ren Shiue0https://orcid.org/0000-0002-6406-3364Ken-Chuan Lee1https://orcid.org/0000-0002-7458-3734Chao-Ton Su2https://orcid.org/0000-0001-7630-1473Department of Management Engineering, Fujian Business University, Fuzhou, ChinaDepartment of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, TaiwanDepartment of Management Engineering, Fujian Business University, Fuzhou, ChinaMachine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.https://ieeexplore.ieee.org/document/9110908/Manufacturing execution systemdynamic schedulingmachine learningreinforcement learningQ-learning |
| spellingShingle | Yeou-Ren Shiue Ken-Chuan Lee Chao-Ton Su A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment IEEE Access Manufacturing execution system dynamic scheduling machine learning reinforcement learning Q-learning |
| title | A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment |
| title_full | A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment |
| title_fullStr | A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment |
| title_full_unstemmed | A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment |
| title_short | A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment |
| title_sort | reinforcement learning approach to dynamic scheduling in a product mix flexibility environment |
| topic | Manufacturing execution system dynamic scheduling machine learning reinforcement learning Q-learning |
| url | https://ieeexplore.ieee.org/document/9110908/ |
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