Research on supply chain planning based on genetic algorithm and long short-term memory
With the integration of intelligent algorithm into the supply chain process, the fficiency of supply chain planning has been further improved through automatic prediction and decision-making. Although intelligent algorithms are developing, their challenges including real-time nature of supply chain...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02015.pdf |
| _version_ | 1811226841226149888 |
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| author | Wang Xu Li Yujie Lu Qizong Qiu Yuchen |
| author_facet | Wang Xu Li Yujie Lu Qizong Qiu Yuchen |
| author_sort | Wang Xu |
| collection | DOAJ |
| description | With the integration of intelligent algorithm into the supply chain process, the fficiency of supply chain planning has been further improved through automatic prediction and decision-making. Although intelligent algorithms are developing, their challenges including real-time nature of supply chain planning and the complexity of scenarios hinder their true potential. In this study, we proposed an improved genetic algorithm (GA)-long short-term memory (LSTM) neural network prediction algorithm to solve various optimization planning problems for the supply chain from suppliers to production enterprises. Specifically, to determine stable suppliers, we first constructed the technique for order preference by similarity to ideal solution (TOPSIS) model to quantitatively evaluate each supplier, and the rationality of the index weight of the TOPSIS algorithm can be enhanced by the entropy method. Finally, the GA and LSTM were used to solve the decision-making and planning problem in raw material supply chain. Our results indicate that the algorithm we proposed can not only efficiently solve the decision planning problem in the raw material supply chain, but it also reasonably analyzes the suppliers quantitatively. |
| first_indexed | 2024-04-12T09:31:39Z |
| format | Article |
| id | doaj.art-bfbb2ff189e742eeae4be12b0703ee5b |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-12T09:31:39Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-bfbb2ff189e742eeae4be12b0703ee5b2022-12-22T03:38:21ZengEDP SciencesITM Web of Conferences2271-20972022-01-01470201510.1051/itmconf/20224702015itmconf_cccar2022_02015Research on supply chain planning based on genetic algorithm and long short-term memoryWang Xu0Li Yujie1Lu Qizong2Qiu Yuchen3School of Artificial Intelligence, Guilin University of Electronic TechnologySchool of Artificial Intelligence, Guilin University of Electronic TechnologySchool of Artificial Intelligence, Guilin University of Electronic TechnologySchool of Artificial Intelligence, Guilin University of Electronic TechnologyWith the integration of intelligent algorithm into the supply chain process, the fficiency of supply chain planning has been further improved through automatic prediction and decision-making. Although intelligent algorithms are developing, their challenges including real-time nature of supply chain planning and the complexity of scenarios hinder their true potential. In this study, we proposed an improved genetic algorithm (GA)-long short-term memory (LSTM) neural network prediction algorithm to solve various optimization planning problems for the supply chain from suppliers to production enterprises. Specifically, to determine stable suppliers, we first constructed the technique for order preference by similarity to ideal solution (TOPSIS) model to quantitatively evaluate each supplier, and the rationality of the index weight of the TOPSIS algorithm can be enhanced by the entropy method. Finally, the GA and LSTM were used to solve the decision-making and planning problem in raw material supply chain. Our results indicate that the algorithm we proposed can not only efficiently solve the decision planning problem in the raw material supply chain, but it also reasonably analyzes the suppliers quantitatively.https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02015.pdfdata miningsupply chain planningentropy-topsisgalstm |
| spellingShingle | Wang Xu Li Yujie Lu Qizong Qiu Yuchen Research on supply chain planning based on genetic algorithm and long short-term memory ITM Web of Conferences data mining supply chain planning entropy-topsis ga lstm |
| title | Research on supply chain planning based on genetic algorithm and long short-term memory |
| title_full | Research on supply chain planning based on genetic algorithm and long short-term memory |
| title_fullStr | Research on supply chain planning based on genetic algorithm and long short-term memory |
| title_full_unstemmed | Research on supply chain planning based on genetic algorithm and long short-term memory |
| title_short | Research on supply chain planning based on genetic algorithm and long short-term memory |
| title_sort | research on supply chain planning based on genetic algorithm and long short term memory |
| topic | data mining supply chain planning entropy-topsis ga lstm |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02015.pdf |
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