バイナリーニューラルネットワークを用いたJITスケジューリング問題の一解法

DOI Web Site 被引用文献1件 参考文献9件

書誌事項

タイトル別名
  • A Solution to the JIT Scheduling Problem Using Binary Neural Network
  • バイナリーニューラル ネットワーク オ モチイタ JIT スケジューリング モンダイ ノ イチカイホウ

この論文をさがす

抄録

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.

収録刊行物

被引用文献 (1)*注記

もっと見る

参考文献 (9)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ