| Title: | Dynamic scheduling for manufacturing workshops using digital twins, competitive particle swarm optimization, and siamese neural networks |
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| Authors: | ID Weng, L. L. (Author) |
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| Files: |  APEM19-3_301-314.pdf (1,36 MB)
MD5: AF4D8ABE664099B24FFA19B0F1C6BB9A
 https://apem-journal.org/Archives/2024/Abstract-APEM19-3_301-314.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Flexible manufacturing workshops often encounter scheduling challenges due to complex processes and cumbersome procedures. To address these issues, a dynamic scheduling method is proposed. Initially, a discrete manufacturing workshop scheduling problem model is developed, considering the unique characteristics of the workshop. Digital Twin technology and a Competitive Particle Swarm Optimization algorithm are then integrated to create the scheduling model. Finally, Siamese Neural Networks are incorporated to form a dynamic scheduling mechanism that optimizes disturbance scheduling. The research model demonstrated a quick convergence, efficiently searching for the optimal fitness value using both the Sphere and Griewank functions. In the scheduling objective function test, the model achieved a maximum completion time of 244.8 minutes, the shortest time compared to similar technologies. In Siamese Neural Network experiments, the model successfully suppressed the influence of disturbances, maintaining optimal scheduling performance. Without adjustments for disturbances, the maximum completion time was 58.5 minutes. After optimization, it decreased to 54.2 minutes. These results demonstrate the effective application of the proposed technology in workshop scheduling. The findings provide valuable technical insights for the application of intelligent technologies in workshop scheduling optimization. |
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| Keywords: | manufacturing workshop, scheduling, digital twin, siamese network, competitive swarm optimization, siamese neural network |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 14.10.2024 |
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| Article acceptance date: | 25.10.2024 |
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| Publication date: | 31.10.2024 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 301-314 |
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| Numbering: | Vol. 19, no. 3 |
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| PID: | 20.500.12556/DKUM-96918  |
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| UDC: | 658.5 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 266946051 |
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| DOI: | 10.14743/apem2024.3.508 |
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| Copyright: | Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
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| Publication date in DKUM: | 02.02.2026 |
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| Views: | 148 |
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| Downloads: | 1 |
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| Metadata: |    |
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| Categories: | Misc.
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