Optimization for Line of Cars Manufacturing Plant using Constrained Genetic Algorithm
In 2007 he obtained his PhD at the Department of Brain Science and Engineering, Kyushu Institute of Technology. From April, 2007 to March, 2014, he was a lecturer. Since April, 2014, he has been an associate professor, Nishinippon Institute of Technology.
- https://doi.org/10.2991/jrnal.2018.5.2.13How to use a DOI?
- Constrained Genetic Algorithm; Plant Optimization; Industrial Application; Car manufacturing
Recently, improvement of production efficiency on cars manufacturers is required. However, that improvements under existing circumstances are depending on experience and intuition by workers. We propose to objectively and efficiently improve a production line based on a GA. The difficulty of applying a GA is the number of racks and boxes is predetermined, and so we apply constrained GA. The results of simulation for virtual production line show that our proposal succeeded in reducing about 10 seconds per a car compared with random positioning.
- Copyright © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Cite this article
TY - JOUR AU - Keiji Kamei AU - Takafumi Arai PY - 2018 DA - 2018/09/30 TI - Optimization for Line of Cars Manufacturing Plant using Constrained Genetic Algorithm JO - Journal of Robotics, Networking and Artificial Life SP - 131 EP - 134 VL - 5 IS - 2 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2018.5.2.13 DO - https://doi.org/10.2991/jrnal.2018.5.2.13 ID - Kamei2018 ER -