Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)

Model of Spare Parts Optimization Based on GA for Equipment

Authors
Guangze Pan, Qin Luo, Xiaobing Li, Yuanhang Wang, Chuangmian Huang
Corresponding Author
Guangze Pan
Available Online July 2018.
DOI
10.2991/msam-18.2018.10How to use a DOI?
Keywords
equipment; genetic algorithm; spare parts optimization
Abstract

Combined with the engineering requirements for the optimal allocation of the current ammunition equipment spare parts, a spare part optimization model based on genetic algorithm was established. With the combination of good readiness and cost of ammunition equipment, the use of genetic algorithm had the characteristics of fast convergence, strong global optimization, and simple programming. The model was solved and the spare parts of ammunition equipment were optimally configured. Finally, an example of ammunition equipment was analyzed. The results show that the genetic algorithm can effectively solve the optimization problem of ammunition equipment spare parts.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
Series
Advances in Intelligent Systems Research
Publication Date
July 2018
ISBN
10.2991/msam-18.2018.10
ISSN
1951-6851
DOI
10.2991/msam-18.2018.10How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Guangze Pan
AU  - Qin Luo
AU  - Xiaobing Li
AU  - Yuanhang Wang
AU  - Chuangmian Huang
PY  - 2018/07
DA  - 2018/07
TI  - Model of Spare Parts Optimization Based on GA for Equipment
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 44
EP  - 47
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.10
DO  - 10.2991/msam-18.2018.10
ID  - Pan2018/07
ER  -