An Enhanced NSGA-II Driven by Deep Reinforcement Learning to Mixed Flow Assembly Workshop Scheduling System with Constraints of Continuous Processing and Mold Changing
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| Publicado en: | Systems vol. 13, no. 8 (2025), p. 659-679 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/systems13080659 |2 doi | |
| 035 | |a 3244062869 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231636 |2 nlm | ||
| 100 | 1 | |a Yang Bihao |u School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China; 20221100321@csuft.edu.cn (B.Y.); 20231100332@csuft.edu.cn (J.C.); 20241100380@csuft.edu.cn (X.X.) | |
| 245 | 1 | |a An Enhanced NSGA-II Driven by Deep Reinforcement Learning to Mixed Flow Assembly Workshop Scheduling System with Constraints of Continuous Processing and Mold Changing | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach struggles to meet the processing constraints of workpieces with higher production difficulty, while the second approach requires the development of suitable scheduling schemes to balance mold changes and continuous processing. Therefore, under the second approach, developing an excellent scheduling scheme is a challenging problem. This study addresses the mixed-flow assembly shop scheduling problem, considering continuous processing and mold-changing constraints, by developing a multi-objective optimization model to minimize additional production time and customer waiting time. As this NP-hard problem poses significant challenges in solution space exploration, the conventional NSGA-II algorithm suffers from limited local search capability. To address this, we propose an enhanced NSGA-II algorithm (RLVNS-NSGA-II) integrating deep reinforcement learning. Our approach combines multiple neighborhood search operators with deep reinforcement learning, which dynamically utilizes population diversity and objective function data to guide and strengthen local search. Simulation experiments confirm that the proposed algorithm surpasses existing methods in local search performance. Compared to VNS-NSGA-II and SVNS-NSGA-II, the RLVNS-NSGA-II algorithm achieved hypervolume improvements ranging from 19.72% to 42.88% and 12.63% to 31.19%, respectively. | |
| 653 | |a Work stations | ||
| 653 | |a Space exploration | ||
| 653 | |a Integer programming | ||
| 653 | |a Workpieces | ||
| 653 | |a Deep learning | ||
| 653 | |a Molds | ||
| 653 | |a Optimization | ||
| 653 | |a Batch processing | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Manufacturing | ||
| 653 | |a Batch production | ||
| 653 | |a Heuristic | ||
| 653 | |a Efficiency | ||
| 653 | |a Optimization models | ||
| 653 | |a Assembly lines | ||
| 653 | |a Scheduling | ||
| 653 | |a Continuous casting | ||
| 653 | |a Operators (mathematics) | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Searching | ||
| 653 | |a Solution space | ||
| 653 | |a Algorithms | ||
| 653 | |a Constraints | ||
| 700 | 1 | |a Chen, Jie |u School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China; 20221100321@csuft.edu.cn (B.Y.); 20231100332@csuft.edu.cn (J.C.); 20241100380@csuft.edu.cn (X.X.) | |
| 700 | 1 | |a Xiao Xiongxin |u School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China; 20221100321@csuft.edu.cn (B.Y.); 20231100332@csuft.edu.cn (J.C.); 20241100380@csuft.edu.cn (X.X.) | |
| 700 | 1 | |a Li, Sidi |u School of Foreign Languages, Central South University of Forestry and Technology, Changsha 410004, China; lisidi@csuft.edu.cn | |
| 700 | 1 | |a Teng, Ren |u School of Economics and Management, Central South University of Forestry and Technology, Changsha 410004, China; 20221100321@csuft.edu.cn (B.Y.); 20231100332@csuft.edu.cn (J.C.); 20241100380@csuft.edu.cn (X.X.) | |
| 773 | 0 | |t Systems |g vol. 13, no. 8 (2025), p. 659-679 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244062869/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244062869/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244062869/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |