Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem
In this paper, the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm is proposed to minimize the maximum completion time in a dynamic scheduling problem of hybrid flow shops. In hybrid flow shops, machines require flexible preventive maintenance and jobs arrive randomly with uncertain proces...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/45/1/25 |
| _version_ | 1797381203797475328 |
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| author | Junjie Zhang Yarong Chen Jabir Mumtaz Shengwei Zhou |
| author_facet | Junjie Zhang Yarong Chen Jabir Mumtaz Shengwei Zhou |
| author_sort | Junjie Zhang |
| collection | DOAJ |
| description | In this paper, the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm is proposed to minimize the maximum completion time in a dynamic scheduling problem of hybrid flow shops. In hybrid flow shops, machines require flexible preventive maintenance and jobs arrive randomly with uncertain processing times. The NEAT-based approach is experimentally compared with the SPT and FIFO scheduling rules by designing problem instances. The results show that the NEAT-based scheduling method can obtain solutions with better convergence while responding quickly compared to the scheduling rules. |
| first_indexed | 2024-03-08T20:48:00Z |
| format | Article |
| id | doaj.art-a9c5190dd7004a638c878042aa0a1efc |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-08T20:48:00Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-a9c5190dd7004a638c878042aa0a1efc2023-12-22T14:06:33ZengMDPI AGEngineering Proceedings2673-45912023-09-014512510.3390/engproc2023045025Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop ProblemJunjie Zhang0Yarong Chen1Jabir Mumtaz2Shengwei Zhou3School of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaSchool of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaSchool of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaSchool of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, ChinaIn this paper, the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm is proposed to minimize the maximum completion time in a dynamic scheduling problem of hybrid flow shops. In hybrid flow shops, machines require flexible preventive maintenance and jobs arrive randomly with uncertain processing times. The NEAT-based approach is experimentally compared with the SPT and FIFO scheduling rules by designing problem instances. The results show that the NEAT-based scheduling method can obtain solutions with better convergence while responding quickly compared to the scheduling rules.https://www.mdpi.com/2673-4591/45/1/25hybrid flow shopreinforcement learningNeuro-Evolution of Augmenting Topologiesmakespandynamic scheduling |
| spellingShingle | Junjie Zhang Yarong Chen Jabir Mumtaz Shengwei Zhou Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem Engineering Proceedings hybrid flow shop reinforcement learning Neuro-Evolution of Augmenting Topologies makespan dynamic scheduling |
| title | Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem |
| title_full | Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem |
| title_fullStr | Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem |
| title_full_unstemmed | Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem |
| title_short | Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Hybrid Flow Shop Problem |
| title_sort | neuro evolution of augmenting topologies for dynamic scheduling of hybrid flow shop problem |
| topic | hybrid flow shop reinforcement learning Neuro-Evolution of Augmenting Topologies makespan dynamic scheduling |
| url | https://www.mdpi.com/2673-4591/45/1/25 |
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