Production scheduling with multi-robot task allocation in a real industry 4.0 setting
Abstract The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplif...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84240-3 |
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| _version_ | 1832594756615536640 |
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| author | Zohreh Shakeri Khaled Benfriha Mohsen Varmazyar Esma Talhi Anthony Quenehen |
| author_facet | Zohreh Shakeri Khaled Benfriha Mohsen Varmazyar Esma Talhi Anthony Quenehen |
| author_sort | Zohreh Shakeri |
| collection | DOAJ |
| description | Abstract The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems. |
| format | Article |
| id | doaj-art-4a03b8b074ff498ca56d761fc3627ece |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4a03b8b074ff498ca56d761fc3627ece2025-01-19T12:22:34ZengNature PortfolioScientific Reports2045-23222025-01-0115112410.1038/s41598-024-84240-3Production scheduling with multi-robot task allocation in a real industry 4.0 settingZohreh Shakeri0Khaled Benfriha1Mohsen Varmazyar2Esma Talhi3Anthony Quenehen4Laboratoire Conception de Produits et Innovation (LCPI), Arts et Metiers Institute of TechnologyLaboratoire Conception de Produits et Innovation (LCPI), Arts et Metiers Institute of TechnologyDepartment of Industrial Engineering, Shairf University of TechnologyEcole d’Ingenieurs en Genie des Systemes Industriels (EIGSI)Laboratoire d’Ingenierie des Systemes Physiques et NumeriquesAbstract The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.https://doi.org/10.1038/s41598-024-84240-3 |
| spellingShingle | Zohreh Shakeri Khaled Benfriha Mohsen Varmazyar Esma Talhi Anthony Quenehen Production scheduling with multi-robot task allocation in a real industry 4.0 setting Scientific Reports |
| title | Production scheduling with multi-robot task allocation in a real industry 4.0 setting |
| title_full | Production scheduling with multi-robot task allocation in a real industry 4.0 setting |
| title_fullStr | Production scheduling with multi-robot task allocation in a real industry 4.0 setting |
| title_full_unstemmed | Production scheduling with multi-robot task allocation in a real industry 4.0 setting |
| title_short | Production scheduling with multi-robot task allocation in a real industry 4.0 setting |
| title_sort | production scheduling with multi robot task allocation in a real industry 4 0 setting |
| url | https://doi.org/10.1038/s41598-024-84240-3 |
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