Paper:
Views over last 60 days: 389
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
Received:April 7, 1992Accepted:September 30, 1992Published:October 20, 1992
Keywords:Production system, Production management, Production planning, Dynamic job-shop scheduling, Hopfield-type neural network, Operations research, Due date, In-process inventory
Abstract
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.
Cite this article as:N. Takatori and Y. Kakazu, “Dynamic Job-Shop Scheduling by Hopfield-Type Neural Network,” J. Robot. Mechatron., Vol.4 No.5, pp. 401-406, 1992.Data files:
