Volume 8, Issue 1, June 2021, Pages 33 - 36
Decision System for Plural Production Lines Layout by Using GA
Authors
Hidehiko Yamamoto1, *, Masato Noda2, Hirohumi Tsuji3, Yasuhisa Terawa3, Yoshinori Nakamura3, Masayuki Tsuchida3, Katsuaki Yamada4, Yukiyasu Kuriyama4
1Department of Mechanical Engineering, Gifu University, Yanagido 1-1, Gifu, Gifu 501-1194, Japan
2KIOXIA Corporation, Shibaura 3-1-21, Tokyo, Japan
3New Business Development Div., InfoFarm Co., Ltd, Yanaizu-cho Distribution Center 1-8-4, Gifu, Gifu 501-6123, Japan
4Production Div., Kai Industries Co., Ltd, Oyana 1110, Seki 501-3992, Japan
*Corresponding author. Email: yam-h@gifu-u.ac.jp
Corresponding Author
Hidehiko Yamamoto
Received 23 October 2020, Accepted 8 April 2021, Available Online 28 May 2021.
- DOI
- 10.2991/jrnal.k.210521.008How to use a DOI?
- Keywords
- Line layout; genetic algorithm; Chameleon-code; production line; walking time
- Abstract
We develop the system to decide the efficient layout of assembly production line by using Genetic Algorithm (GA). We call the system as Decision System of Plural Production-lines-layout by GA (PPG). PPG decides the efficient layout of production line by using GA, work-flow-line acquired by Chameleon code and the machine breakdown data. PPG evaluates the layout efficiency by calculating the operator’s walking time to fix the machine breakdown occurred on the production line. By the evaluation, it is ascertained that PPG was useful.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Hidehiko Yamamoto AU - Masato Noda AU - Hirohumi Tsuji AU - Yasuhisa Terawa AU - Yoshinori Nakamura AU - Masayuki Tsuchida AU - Katsuaki Yamada AU - Yukiyasu Kuriyama PY - 2021 DA - 2021/05/28 TI - Decision System for Plural Production Lines Layout by Using GA JO - Journal of Robotics, Networking and Artificial Life SP - 33 EP - 36 VL - 8 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.210521.008 DO - 10.2991/jrnal.k.210521.008 ID - Yamamoto2021 ER -