A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM
Enterprise production is often interfered with by internal and external factors, resulting in the infeasible original production scheduling scheme. In terms of this issue, it is necessary to quickly decide the optimal production scheduling scheme after these disturbances so that the enterprise is pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/2/59 |
| _version_ | 1827755435435753472 |
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| author | Lijun Song Zhipeng Xu Chengfu Wang Jiafu Su |
| author_facet | Lijun Song Zhipeng Xu Chengfu Wang Jiafu Su |
| author_sort | Lijun Song |
| collection | DOAJ |
| description | Enterprise production is often interfered with by internal and external factors, resulting in the infeasible original production scheduling scheme. In terms of this issue, it is necessary to quickly decide the optimal production scheduling scheme after these disturbances so that the enterprise is produced efficiently. Therefore, this paper proposes a new rescheduling decision model based on the whale optimization algorithm and support vector machine (WOA-SVM). Firstly, the disturbance in the production process is simulated, and the dimensionality of the data from the simulation is reduced to train the machine learning model. Then, this trained model is combined with the rescheduling schedule to deal with the disturbance in the actual production. The experimental results show that the support vector machine (SVM) performs well in solving classification and decision problems. Moreover, the WOA-SVM can solve problems more quickly and accurately compared to the traditional SVM. The WOA-SVM can predict the flexible job shop rescheduling mode with an accuracy of 89.79%. It has higher stability compared to other machine learning methods. This method can respond to the disturbance in production in time and satisfy the needs of modern enterprises for intelligent production. |
| first_indexed | 2024-03-11T08:04:19Z |
| format | Article |
| id | doaj.art-d6bfdfd9779543168f3c6573d1cb68f2 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-11T08:04:19Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-d6bfdfd9779543168f3c6573d1cb68f22023-11-16T23:35:09ZengMDPI AGSystems2079-89542023-01-011125910.3390/systems11020059A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVMLijun Song0Zhipeng Xu1Chengfu Wang2Jiafu Su3School of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Management, Chongqing University of Technology, Chongqing 400054, ChinaInternational College, Krirk University, Bangkok 10220, ThailandEnterprise production is often interfered with by internal and external factors, resulting in the infeasible original production scheduling scheme. In terms of this issue, it is necessary to quickly decide the optimal production scheduling scheme after these disturbances so that the enterprise is produced efficiently. Therefore, this paper proposes a new rescheduling decision model based on the whale optimization algorithm and support vector machine (WOA-SVM). Firstly, the disturbance in the production process is simulated, and the dimensionality of the data from the simulation is reduced to train the machine learning model. Then, this trained model is combined with the rescheduling schedule to deal with the disturbance in the actual production. The experimental results show that the support vector machine (SVM) performs well in solving classification and decision problems. Moreover, the WOA-SVM can solve problems more quickly and accurately compared to the traditional SVM. The WOA-SVM can predict the flexible job shop rescheduling mode with an accuracy of 89.79%. It has higher stability compared to other machine learning methods. This method can respond to the disturbance in production in time and satisfy the needs of modern enterprises for intelligent production.https://www.mdpi.com/2079-8954/11/2/59flexible job shoprescheduling modelmachine learningwhale optimization algorithmsupport vector machine |
| spellingShingle | Lijun Song Zhipeng Xu Chengfu Wang Jiafu Su A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM Systems flexible job shop rescheduling model machine learning whale optimization algorithm support vector machine |
| title | A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM |
| title_full | A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM |
| title_fullStr | A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM |
| title_full_unstemmed | A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM |
| title_short | A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM |
| title_sort | new decision method of flexible job shop rescheduling based on woa svm |
| topic | flexible job shop rescheduling model machine learning whale optimization algorithm support vector machine |
| url | https://www.mdpi.com/2079-8954/11/2/59 |
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