A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling
Abstract A novel algorithm combining bidirectional recurrent neural networks (BiRNNs) with temporal difference is proposed for multi‐objective energy‐efficient non‐permutation flow‐shop scheduling problem (NFSP). The objectives of this problem involve minimising both the makespan and total energy co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Collaborative Intelligent Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cim2.12121 |
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| _version_ | 1846102054580256768 |
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| author | Peng Liang Pengfei Xiao Zeya Li Min Luo Chaoyong Zhang |
| author_facet | Peng Liang Pengfei Xiao Zeya Li Min Luo Chaoyong Zhang |
| author_sort | Peng Liang |
| collection | DOAJ |
| description | Abstract A novel algorithm combining bidirectional recurrent neural networks (BiRNNs) with temporal difference is proposed for multi‐objective energy‐efficient non‐permutation flow‐shop scheduling problem (NFSP). The objectives of this problem involve minimising both the makespan and total energy consumption. To begin, a mathematical model is formulated to represent the energy‐efficient NFSP. Subsequently, the NFSP is transformed into a Markov decision process, where an action space comprising 28 scheduling rules is constructed. Considering the global and local features of NFSP, a set of 15 state features is extracted. Different reward functions are then defined to correspond to the specific objectives. Furthermore, the state features of NFSP are extracted using a multi‐layer perceptron model based on BiRNNs. By utilising the TD(λ) algorithm to calculate the state value function, various policies are generated. In order to evaluate the proposed algorithm, a new test set for the energy‐efficient NFSP is constructed, building upon classic benchmark problems. Finally, comparison experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm. |
| format | Article |
| id | doaj-art-52e7ceca5c1e41f7a1196bd4df1e6583 |
| institution | Kabale University |
| issn | 2516-8398 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj-art-52e7ceca5c1e41f7a1196bd4df1e65832024-12-28T04:20:30ZengWileyIET Collaborative Intelligent Manufacturing2516-83982024-12-0164n/an/a10.1049/cim2.12121A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop schedulingPeng Liang0Pengfei Xiao1Zeya Li2Min Luo3Chaoyong Zhang4School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan ChinaSchool of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan ChinaAbstract A novel algorithm combining bidirectional recurrent neural networks (BiRNNs) with temporal difference is proposed for multi‐objective energy‐efficient non‐permutation flow‐shop scheduling problem (NFSP). The objectives of this problem involve minimising both the makespan and total energy consumption. To begin, a mathematical model is formulated to represent the energy‐efficient NFSP. Subsequently, the NFSP is transformed into a Markov decision process, where an action space comprising 28 scheduling rules is constructed. Considering the global and local features of NFSP, a set of 15 state features is extracted. Different reward functions are then defined to correspond to the specific objectives. Furthermore, the state features of NFSP are extracted using a multi‐layer perceptron model based on BiRNNs. By utilising the TD(λ) algorithm to calculate the state value function, various policies are generated. In order to evaluate the proposed algorithm, a new test set for the energy‐efficient NFSP is constructed, building upon classic benchmark problems. Finally, comparison experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.https://doi.org/10.1049/cim2.12121deep reinforcement learningflow shop schedulinggreen manufacturingoptimisation |
| spellingShingle | Peng Liang Pengfei Xiao Zeya Li Min Luo Chaoyong Zhang A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling IET Collaborative Intelligent Manufacturing deep reinforcement learning flow shop scheduling green manufacturing optimisation |
| title | A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling |
| title_full | A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling |
| title_fullStr | A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling |
| title_full_unstemmed | A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling |
| title_short | A novel deep reinforcement learning‐based algorithm for multi‐objective energy‐efficient flow‐shop scheduling |
| title_sort | novel deep reinforcement learning based algorithm for multi objective energy efficient flow shop scheduling |
| topic | deep reinforcement learning flow shop scheduling green manufacturing optimisation |
| url | https://doi.org/10.1049/cim2.12121 |
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