A distributed learning method for due date assignment in flexible job shops

Wei Weng, Gang Rong, Shigeru Fujimura

研究成果: Conference article査読


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.

ジャーナルCEUR Workshop Proceedings
出版ステータスPublished - 2016
イベント9th International Conference on Discrete Optimization and Operations Research, DOOR 2016 - Vladivostok, Russian Federation
継続期間: 2016 9月 192016 9月 23

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)


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