Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
A Solution to the JIT Scheduling Problem Using Binary Neural Network
Akihiro MATSUNAGAYuki NAKASHIMAAkihiro KANAGAWAIkuo ARIZONO
Author information
JOURNALS FREE ACCESS

2001 Volume 14 Issue 11 Pages 530-535

Details
Abstract

Recently, Just-In-Time (JIT) production systems have much influence on the production fields. Miyazaki et al. propose a concept of actual flow time, which is a performance measure for scheduling in a JIT production environment. Scheduling problems are one of representative combinatorial optimization problems. Hopfield and Tank show that some combinatorial optimization problems can be solved by the artificial neural network system. Arizono et al. propose a neural solution for minimizing total actual flow time by the Gaussian machine. However, their method retains some problems which originate in the analog neurons. Then, we use interconnected neural networks which consist of the binary neurons whose output states take values either 0 or 1 unlike the Arizono's system. We call such a network the binary neural network. This paper deals with the scheduling problem for minimizing total actual flow time by the binary neural network.

Information related to the author
© The Institute of Systems, Control and Information Engineers
Previous article Next article
feedback
Top