Dynamic Job-Shop Scheduling by Hopfield-Type Neural Network
Norihiko Takatori* and Yukinori Kakazu**
*Hokkaido College of Arts and Sciences, 582-1, Midori-machi, Bunkyou-dai, Ebetu, Hokkaido 069, Japan
**Faculty of Engineering, Hokkaido University, Kita-13, Nishi-8, Sapporo, Hokkaido 060, Japan
This paper deals with an approach to the dynamic jobshop scheduling problem. In this approach, the Hopfield-type neural network is introduced for solving the problem. The idea is based on the mapping between the scheduling problem and the neural network. That is, the energy function of the network is set for the problem so that a job assignment corresponds to the equilibrium of the network. The solution of the scheduling problem is obtained when the network is in equilibrium. In this paper, the method of constructing the energy function with due date and in-process inventory as criteria is described, and reasonable results of several numerical experiments are shown.
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