An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems
With the rise of Industry 4.0, manufacturing is shifting towards customization and flexibility, presenting new challenges to meet rapidly evolving market and customer needs. To address these challenges, this paper suggests a novel approach to address flexible job shop scheduling problems (FJSPs) thr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-01-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024062?viewType=HTML |
| _version_ | 1797326308323098624 |
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| author | Cong Zhao Na Deng |
| author_facet | Cong Zhao Na Deng |
| author_sort | Cong Zhao |
| collection | DOAJ |
| description | With the rise of Industry 4.0, manufacturing is shifting towards customization and flexibility, presenting new challenges to meet rapidly evolving market and customer needs. To address these challenges, this paper suggests a novel approach to address flexible job shop scheduling problems (FJSPs) through reinforcement learning (RL). This method utilizes an actor-critic architecture that merges value-based and policy-based approaches. The actor generates deterministic policies, while the critic evaluates policies and guides the actor to achieve the most optimal policy. To construct the Markov decision process, a comprehensive feature set was utilized to accurately represent the system's state, and eight sets of actions were designed, inspired by traditional scheduling rules. The formulation of rewards indirectly measures the effectiveness of actions, promoting strategies that minimize job completion times and enhance adherence to scheduling constraints. The experimental evaluation conducted a thorough assessment of the proposed reinforcement learning framework through simulations on standard FJSP benchmarks, comparing the proposed method against several well-known heuristic scheduling rules, related RL algorithms and intelligent algorithms. The results indicate that the proposed method consistently outperforms traditional approaches and exhibits exceptional adaptability and efficiency, particularly in large-scale datasets. |
| first_indexed | 2024-03-08T06:22:27Z |
| format | Article |
| id | doaj.art-8d3cb687e0d1448e90447d16070ba218 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-08T06:22:27Z |
| publishDate | 2024-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-8d3cb687e0d1448e90447d16070ba2182024-02-04T01:31:42ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-01-012111445147110.3934/mbe.2024062An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problemsCong Zhao0Na Deng1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaWith the rise of Industry 4.0, manufacturing is shifting towards customization and flexibility, presenting new challenges to meet rapidly evolving market and customer needs. To address these challenges, this paper suggests a novel approach to address flexible job shop scheduling problems (FJSPs) through reinforcement learning (RL). This method utilizes an actor-critic architecture that merges value-based and policy-based approaches. The actor generates deterministic policies, while the critic evaluates policies and guides the actor to achieve the most optimal policy. To construct the Markov decision process, a comprehensive feature set was utilized to accurately represent the system's state, and eight sets of actions were designed, inspired by traditional scheduling rules. The formulation of rewards indirectly measures the effectiveness of actions, promoting strategies that minimize job completion times and enhance adherence to scheduling constraints. The experimental evaluation conducted a thorough assessment of the proposed reinforcement learning framework through simulations on standard FJSP benchmarks, comparing the proposed method against several well-known heuristic scheduling rules, related RL algorithms and intelligent algorithms. The results indicate that the proposed method consistently outperforms traditional approaches and exhibits exceptional adaptability and efficiency, particularly in large-scale datasets.https://www.aimspress.com/article/doi/10.3934/mbe.2024062?viewType=HTMLflexible job shop scheduling problemsdeep reinforcement learningactor-critic methodmarkov decision processdeep neural networks |
| spellingShingle | Cong Zhao Na Deng An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems Mathematical Biosciences and Engineering flexible job shop scheduling problems deep reinforcement learning actor-critic method markov decision process deep neural networks |
| title | An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems |
| title_full | An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems |
| title_fullStr | An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems |
| title_full_unstemmed | An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems |
| title_short | An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems |
| title_sort | actor critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems |
| topic | flexible job shop scheduling problems deep reinforcement learning actor-critic method markov decision process deep neural networks |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024062?viewType=HTML |
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