Paper Title:
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.

  Info
Periodical
Advanced Materials Research (Volumes 314-316)
Chapter
Digital Manufacture and Management
Edited by
Jian Gao
Pages
2172-2176
DOI
10.4028/www.scientific.net/AMR.314-316.2172
Citation
C. Wang, H. B. Zhang, J. Guo, L. Chen, "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
$32.00
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