Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics
Efficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook t...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10813358/ |
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| author | Dmitrii Fomin Ilya Makarov Mariia Voronina Anna Strimovskaya Vitaliy Pozdnyakov |
| author_facet | Dmitrii Fomin Ilya Makarov Mariia Voronina Anna Strimovskaya Vitaliy Pozdnyakov |
| author_sort | Dmitrii Fomin |
| collection | DOAJ |
| description | Efficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook these factors. To address this gap, we investigate task scheduling in cloud manufacturing systems, emphasizing logistics integration. We propose a novel Graph Neural Network architecture for optimizing task scheduling by representing the problem on a heterogeneous graph, where nodes denote tasks and locations. Our model minimizes both manufacturing and logistics costs, achieving significant performance improvements over greedy algorithms and comparable results to strong genetic algorithms in large-scale scenarios with up to 20 locations. This work advances the efficiency and flexibility of cloud manufacturing systems, offering practical solutions for dynamic, cost-sensitive environments. |
| format | Article |
| id | doaj-art-a869586ba72e4126a738e4b8e5d3208a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a869586ba72e4126a738e4b8e5d3208a2025-08-20T02:58:18ZengIEEEIEEE Access2169-35362024-01-011219619519620610.1109/ACCESS.2024.352202010813358Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and LogisticsDmitrii Fomin0https://orcid.org/0009-0009-5953-721XIlya Makarov1https://orcid.org/0000-0002-3308-8825Mariia Voronina2https://orcid.org/0009-0002-5122-498XAnna Strimovskaya3https://orcid.org/0000-0003-0332-1494Vitaliy Pozdnyakov4https://orcid.org/0000-0003-4369-4068Moscow Institute of Physics and Technology, Moscow, RussiaISP RAS, Moscow, RussiaIITP RAS, Moscow, RussiaSt.Petersburg School of Economics and Management, HSE University, Saint Petersburg, RussiaAIRI, Moscow, RussiaEfficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook these factors. To address this gap, we investigate task scheduling in cloud manufacturing systems, emphasizing logistics integration. We propose a novel Graph Neural Network architecture for optimizing task scheduling by representing the problem on a heterogeneous graph, where nodes denote tasks and locations. Our model minimizes both manufacturing and logistics costs, achieving significant performance improvements over greedy algorithms and comparable results to strong genetic algorithms in large-scale scenarios with up to 20 locations. This work advances the efficiency and flexibility of cloud manufacturing systems, offering practical solutions for dynamic, cost-sensitive environments.https://ieeexplore.ieee.org/document/10813358/Cloud manufacturinglogisticsgraph neural networkstask schedulingindustry 4.0 |
| spellingShingle | Dmitrii Fomin Ilya Makarov Mariia Voronina Anna Strimovskaya Vitaliy Pozdnyakov Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics IEEE Access Cloud manufacturing logistics graph neural networks task scheduling industry 4.0 |
| title | Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics |
| title_full | Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics |
| title_fullStr | Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics |
| title_full_unstemmed | Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics |
| title_short | Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics |
| title_sort | heterogeneous graph attention networks for scheduling in cloud manufacturing and logistics |
| topic | Cloud manufacturing logistics graph neural networks task scheduling industry 4.0 |
| url | https://ieeexplore.ieee.org/document/10813358/ |
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