Reinforcement Learning Based Job Shop Scheduling with Machine Choice

Chao Wang; Hong Bin Zhang; Jing Guo; Ling Chen

doi:10.4028/www.scientific.net/AMR.314-316.2172

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 314-316Reinforcement Learning Based Job Shop Scheduling...

Reinforcement Learning Based Job Shop Scheduling with Machine Choice

Abstract:

Job shop scheduling is a key technology in modern manufacturing. Scheduling performance will decide the enterprises’ core competitiveness. In this paper, improved reinforcement learning with cohesion is used in dynamic job shop environment, and it eased the contradiction of precocious and slow convergence. Also the machine choice is considered. So the dual scheduling which included job and machine is achieved in this system. And it obtains better results through the experiments. The utilization of equipments and the emergency handling capacity can be improved in the dynamic environment.

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Periodical:

Advanced Materials Research (Volumes 314-316)

Pages:

2172-2176

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.314-316.2172

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Online since:

August 2011

Authors:

Chao Wang, Hong Bin Zhang, Jing Guo, Ling Chen

Keywords:

Cohesion, Dynamic Scheduling, Machine Choice, Reinforcement Learning

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References

[1] Garey, M.R. Johnson, D.S. and Sethi,R.: Mathematics of Operations Research (1976), p.117

Google Scholar

[2] Wang Ling: Shop scheduling with genetic algorithm.TsingHua University Press(2003), pp.59-67

Google Scholar

[3] Huang, D.S., Ip, H.S., Law, K.C.K.: IEEE Transanction on Neural Networks (2005), p.721

Google Scholar

[4] Jain, A. and Meeran, S. : European Journal of Operational Research (1999),390-434

Google Scholar

[5] Leslie Pack Kaelbling, Michael L.Littman.: Journal of Artificial Intelligence Research.Vol.4(1996).p.237.

Google Scholar

[6] Wei Ying-Zi, Zhao Ming-Yang.: ACTA AUTOMATICA SINICA.Vol.31(2005).

Google Scholar

[7] Yi-Chi Wang, John M. Usher. APPLICATION OF REINFORCEMENT LEARNING MULTI-AGENT PRODUCTION SCHEDULING. Mississippi State University(2003).

Google Scholar

[8] CHEN Ling, SHEN Jie, QIN Ling, CHEN Hong-Jian.: Journal of Software.Vol.14(2003), p.1379.

Google Scholar