Reinforcement Learning Based Job Shop Scheduling with Machine Choice

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 314-316)

Edited by:

Jian Gao

Pages:

2172-2176

DOI:

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

Citation:

C. Wang et al., "Reinforcement Learning Based Job Shop Scheduling with Machine Choice", Advanced Materials Research, Vols. 314-316, pp. 2172-2176, 2011

Online since:

August 2011

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Price:

$35.00

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