Abstract
This study intends to help manufacturers that use flexible job shops improve performance of due date assignment, that is, setting delivery times to jobs that arrive dynamically. High performing due date assignment enables achieving on-time delivery and quick response of delivery time to customer orders. Traditional methods for due date assignment are predefined equations that estimate the duration of making a product in the production system. Such equations are sufficient for relatively simple systems such as single machine shops, but are not very high in accuracy for complex systems such as flexible job shops. To improve due date assignment for such systems, we propose a more flexible method that uses distributed learning to learn the remaining time of a job inside the system. We let each workstation in the production shop be a distributed unit that updates its local queuing time and interacts with other units to provide the total remaining time of a job. We carry out extensive computational experiments to evaluate performance of the proposed method, and the results show that it outperforms two advanced equational methods in terms of both accuracy of estimation and stability in performance.
| Original language | English |
|---|---|
| Pages (from-to) | 791-798 |
| Number of pages | 8 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1623 |
| Publication status | Published - 2016 |
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Keywords
- Artificial intelligence in production
- Distributed system
- Due date assignment
ASJC Scopus subject areas
- Computer Science(all)
Cite this
A distributed learning method for due date assignment in flexible job shops. / Weng, Wei; Rong, Gang; Fujimura, Shigeru.
In: CEUR Workshop Proceedings, Vol. 1623, 2016, p. 791-798.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A distributed learning method for due date assignment in flexible job shops
AU - Weng, Wei
AU - Rong, Gang
AU - Fujimura, Shigeru
PY - 2016
Y1 - 2016
N2 - This study intends to help manufacturers that use flexible job shops improve performance of due date assignment, that is, setting delivery times to jobs that arrive dynamically. High performing due date assignment enables achieving on-time delivery and quick response of delivery time to customer orders. Traditional methods for due date assignment are predefined equations that estimate the duration of making a product in the production system. Such equations are sufficient for relatively simple systems such as single machine shops, but are not very high in accuracy for complex systems such as flexible job shops. To improve due date assignment for such systems, we propose a more flexible method that uses distributed learning to learn the remaining time of a job inside the system. We let each workstation in the production shop be a distributed unit that updates its local queuing time and interacts with other units to provide the total remaining time of a job. We carry out extensive computational experiments to evaluate performance of the proposed method, and the results show that it outperforms two advanced equational methods in terms of both accuracy of estimation and stability in performance.
AB - This study intends to help manufacturers that use flexible job shops improve performance of due date assignment, that is, setting delivery times to jobs that arrive dynamically. High performing due date assignment enables achieving on-time delivery and quick response of delivery time to customer orders. Traditional methods for due date assignment are predefined equations that estimate the duration of making a product in the production system. Such equations are sufficient for relatively simple systems such as single machine shops, but are not very high in accuracy for complex systems such as flexible job shops. To improve due date assignment for such systems, we propose a more flexible method that uses distributed learning to learn the remaining time of a job inside the system. We let each workstation in the production shop be a distributed unit that updates its local queuing time and interacts with other units to provide the total remaining time of a job. We carry out extensive computational experiments to evaluate performance of the proposed method, and the results show that it outperforms two advanced equational methods in terms of both accuracy of estimation and stability in performance.
KW - Artificial intelligence in production
KW - Distributed system
KW - Due date assignment
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UR - http://www.scopus.com/inward/citedby.url?scp=85019754605&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85019754605
VL - 1623
SP - 791
EP - 798
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
ER -