A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing
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| Publicado en: | Measurement and Control vol. 58, no. 5 (May 2025), p. 614 |
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3201726493 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0020-2940 | ||
| 022 | |a 2051-8730 | ||
| 024 | 7 | |a 10.1177/00202940241269643 |2 doi | |
| 035 | |a 3201726493 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 100 | 1 | |a Zhang, Peng |u Shanghai Engineering Research Center of industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China | |
| 245 | 1 | |a A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing | |
| 260 | |b Sage Publications Ltd. |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. Pioneer researches on multi-objective batch machines scheduling mainly focus on evolutionary algorithms, failing to meet the online scheduling demand. To deal with the challenges confronted by incompatible job families, dynamic job arrivals, capacitated machines and multiple objectives, we propose a clustering-aided multi-agent deep reinforcement learning approach (CA-MADRL) for the scheduling problem. Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. To alleviate time complexity of model training, a parameter sharing strategy is introduced between different reinforcement learning base model. By validating the proposed approach with 16 instances designed based on actual production data from a semiconductor manufacturing company, it has been demonstrated that the approach not only meets the high-frequency scheduling requirements of manufacturing systems for parallel batch processing machines but also effectively reduces the total job tardiness and machine energy consumption. | |
| 653 | |a Production management | ||
| 653 | |a Scheduling | ||
| 653 | |a Deep learning | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Clustering | ||
| 653 | |a Pareto optimization | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Batch processing | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Manufacturing | ||
| 653 | |a Computer aided scheduling | ||
| 653 | |a Semiconductors | ||
| 653 | |a Energy consumption | ||
| 653 | |a Sorting algorithms | ||
| 653 | |a Evolutionary algorithms | ||
| 700 | 1 | |a Jin, Mengyu |u Shanghai Engineering Research Center of industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China | |
| 700 | 1 | |a Wang, Ming |u College of Mechanical Engineering, Donghua University, Shanghai, China | |
| 700 | 1 | |a Zhang, Jie |u Shanghai Engineering Research Center of industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China | |
| 700 | 1 | |a He, Junjie |u Shanghai Engineering Research Center of industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China | |
| 700 | 1 | |a Zheng, Peng |u College of Log istics Engineering, Shanghai Maritime University, Shanghai, China | |
| 773 | 0 | |t Measurement and Control |g vol. 58, no. 5 (May 2025), p. 614 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3201726493/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://journals.sagepub.com/doi/10.1177/00202940241269643 |