Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility
This paper addresses the dual-resource constrained flexible job shop scheduling problem (DRCFJSP) with minimizing energy consumption. It is the first to study the energy-conscious DRCFJSP with turn OFF/ ON strategy. Different from the classical FJSP, the worker flexibility is considered in DRCFJSP....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8713461/ |
| _version_ | 1818415908388864000 |
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| author | Leilei Meng Chaoyong Zhang Biao Zhang Yaping Ren |
| author_facet | Leilei Meng Chaoyong Zhang Biao Zhang Yaping Ren |
| author_sort | Leilei Meng |
| collection | DOAJ |
| description | This paper addresses the dual-resource constrained flexible job shop scheduling problem (DRCFJSP) with minimizing energy consumption. It is the first to study the energy-conscious DRCFJSP with turn OFF/ ON strategy. Different from the classical FJSP, the worker flexibility is considered in DRCFJSP. First, in order to solve this problem, we propose two mixed integer linear programming (MILP) models based on two modeling ideas, namely, idle time and idle energy. Because DRCFJSP is NP-hard, then we propose an efficient variable neighborhood search (VNS) algorithm. In the proposed VNS algorithm, eight neighborhood structures are designed to generate neighboring solutions. In addition, four energy-saving decoding approaches are specifically designed, in which two energy-saving strategies, namely, postponing strategy and turn OFF/ ON strategy are designed. Finally, the MILP model, the energy-conscious decoding methods, and the VNS are evaluated on numerical tests, whose effectiveness is shown by the experimental results. The experimental results show that the MILP model based on idle energy performs better than the model based on idle time idea, and the greedy hybrid decoding method outperforms the other three decoding methods. Moreover, the proposed VNS with eight neighborhood structures is a very competitive algorithm for the energy-conscious DRCFJSP. |
| first_indexed | 2024-12-14T11:42:28Z |
| format | Article |
| id | doaj.art-dd2f849c70cd4f479c294a5b3a976a5e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:42:28Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dd2f849c70cd4f479c294a5b3a976a5e2022-12-21T23:02:46ZengIEEEIEEE Access2169-35362019-01-017680436805910.1109/ACCESS.2019.29164688713461Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker FlexibilityLeilei Meng0https://orcid.org/0000-0003-1439-4832Chaoyong Zhang1Biao Zhang2Yaping Ren3State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Computer Science, Liaocheng University, Liaocheng, ChinaDepartment of Industrial Engineering, School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, ChinaThis paper addresses the dual-resource constrained flexible job shop scheduling problem (DRCFJSP) with minimizing energy consumption. It is the first to study the energy-conscious DRCFJSP with turn OFF/ ON strategy. Different from the classical FJSP, the worker flexibility is considered in DRCFJSP. First, in order to solve this problem, we propose two mixed integer linear programming (MILP) models based on two modeling ideas, namely, idle time and idle energy. Because DRCFJSP is NP-hard, then we propose an efficient variable neighborhood search (VNS) algorithm. In the proposed VNS algorithm, eight neighborhood structures are designed to generate neighboring solutions. In addition, four energy-saving decoding approaches are specifically designed, in which two energy-saving strategies, namely, postponing strategy and turn OFF/ ON strategy are designed. Finally, the MILP model, the energy-conscious decoding methods, and the VNS are evaluated on numerical tests, whose effectiveness is shown by the experimental results. The experimental results show that the MILP model based on idle energy performs better than the model based on idle time idea, and the greedy hybrid decoding method outperforms the other three decoding methods. Moreover, the proposed VNS with eight neighborhood structures is a very competitive algorithm for the energy-conscious DRCFJSP.https://ieeexplore.ieee.org/document/8713461/Energy-savingflexible job shop schedulingdual-resourcemixed integer linear programmingvariable neighbourhood search |
| spellingShingle | Leilei Meng Chaoyong Zhang Biao Zhang Yaping Ren Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility IEEE Access Energy-saving flexible job shop scheduling dual-resource mixed integer linear programming variable neighbourhood search |
| title | Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility |
| title_full | Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility |
| title_fullStr | Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility |
| title_full_unstemmed | Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility |
| title_short | Mathematical Modeling and Optimization of Energy-Conscious Flexible Job Shop Scheduling Problem With Worker Flexibility |
| title_sort | mathematical modeling and optimization of energy conscious flexible job shop scheduling problem with worker flexibility |
| topic | Energy-saving flexible job shop scheduling dual-resource mixed integer linear programming variable neighbourhood search |
| url | https://ieeexplore.ieee.org/document/8713461/ |
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