Paper Title:
A Game Theoretic Approach to Job Shop Scheduling
  Abstract

This paper proposes a non-cooperative game approach based on neural network (GMBNN) to solve the job shop scheduling problem. Machines in manufacturing task are defined as players and strategies consist of all the feasible programs which are selected by dispatching rules for minimizing the mean flowtime. Strategies for the game model are generated from a backpropagation neural network, which selects combination of the rules for the machines. Case study shows that the GMBNN can be an effective approach to solve the job shop scheduling problem.

  Info
Periodical
Edited by
Honghua Tan
Pages
960-965
DOI
10.4028/www.scientific.net/AMM.66-68.960
Citation
W. R. Jiang, C. Lu, F. Z. Li, "A Game Theoretic Approach to Job Shop Scheduling", Applied Mechanics and Materials, Vols. 66-68, pp. 960-965, 2011
Online since
July 2011
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