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#5431. Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem
| August 2026 | publication date |
| Proposal available till | 15-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Statistics and Probability; Control and Optimization; Strategy and Management; Management Science and Operations Research; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5431.1   | Contract №5431.2 | Contract №5431.3 | Contract №5431.4 |
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2 place - free (for sale)
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Abstract:
Traditionally, mathematical optimization methods have been applied in manufacturing industries where production scheduling is one of the most important problems and is being actively researched. Extant studies assume that processing times are known or follow a simple distribution. However, the actual processing time in a factory is often unknown and likely follows a complex distribution. Therefore, in this study, we consider estimating the processing time using a machine-learning model. Although there are studies that use machine learning for scheduling optimization itself, it should be noted that the purpose of this study is to estimate an unknown processing time. Using machine-learning models, one can estimate processing times that follow an unknown and complex distribution while further improving the schedule using the computed importance variable. Based on the above, we propose a system for estimating the processing time using machine-learning models when the processing time follows a complex distribution in actual factory data. The advantages of the proposed system are its versatility and applicability to a real-world factory where the processing times are often unknown. The proposed method was evaluated using process information with the processing time for each manufacturing sample provided by research partner companies.
Keywords:
Artificial neural networks; Gaussian process regression; Gradient boosted decision trees; Identical parallel machine scheduling; Machine learning; Operations research
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Traditionally, mathematical optimization methods have been applied in manufacturing industries where production scheduling is one of the most important problems and is being actively researched. Extant studies assume that processing times are known or follow a simple distribution. However, the actual processing time in a factory is often unknown and likely follows a complex distribution. Therefore, in this study, we consider estimating the processing time using a machine-learning model. Although there are studies that use machine learning for scheduling optimization itself, it should be noted that the purpose of this study is to estimate an unknown processing time. Using machine-learning models, one can estimate processing times that follow an unknown and complex distribution while further improving the schedule using the computed importance variable. Based on the above, we propose a system for estimating the processing time using machine-learning models when the processing time follows a complex distribution in actual factory data. The advantages of the proposed system are its versatility and applicability to a real-world factory where the processing times are often unknown. The proposed method was evaluated using process information with the processing time for each manufacturing sample provided by research partner companies.
Keywords:
Artificial neural networks; Gaussian process regression; Gradient boosted decision trees; Identical parallel machine scheduling; Machine learning; Operations research
Contacts :
