A Solution to the JIT Scheduling Problem Using Binary Neural Network
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- MATSUNAGA Akihiro
- Graduate School of Systems Engineering, Okayama Prefectural University
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- NAKASHIMA Yuki
- Mitsui Zosen System Reserch Inc.
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- KANAGAWA Akihiro
- Faculty of Computer Science and System Engineering, Okayama Prefectural University
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- ARIZONO Ikuo
- Graduate School of Engineering, Osaka Prefecture University
Bibliographic Information
- Other Title
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- 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
- 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
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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.
Journal
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- Transactions of the Institute of Systems, Control and Information Engineers
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Transactions of the Institute of Systems, Control and Information Engineers 14 (11), 530-535, 2001
THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)
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Details 詳細情報について
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- CRID
- 1390001205165037696
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- NII Article ID
- 10007136866
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- NII Book ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL BIB ID
- 5962672
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed
