Author(s)

Haoyu Luo ¹, Wei Wang ¹, Lizhu Li ¹, Xiuzhi Huang ¹

Affiliation(s)

¹ Guangzhou South China Business Trade College, Guangzhou, China

Corresponding Author

Wei Wang

ABSTRACT

The supply of initial spares is not practical. As for the actual supply of airlines, its supply reliability is still relatively low, which leads to the contradiction between supply and demand of spares and restricts the capacity formation rate to some extent. The relationship between the variety and quantity of initial spares will directly affect the economic benefits of airlines. Based on the analysis of the characteristics of initial aviation spares, the mathematical method, rough set, is adopted to analyze and determine the types of initial aviation spares, and the   model in grey theory is used to quantitatively predict and analyze the initial aviation spares, which provides a new analysis idea for determining the demand of aviation equipment and a new decision-making method for determining the initial aviation spares, whose prediction accuracy is higher than that of traditional methods.

KEYWORDS

Air materials, initial spares, grey theory, rough set, predictive parsing

CITE THIS PAPER

Haoyu Luo, Wei Wang, Lizhu Li, Xiuzhi Huang, Research on Initial Aviation Spares Based on Rough Set and GM(1,1) Model. Industrial Engineering and Innovation Management (2023) Vol. 6: 23-29. DOI: http://dx.doi.org/10.23977/ieim.2023.060204.

REFERENCES

[1] A Glenn M W. Initial Provisioning/Initial Spares: A Question of Semantics and Cost. Army Aviation Research and Development Command St Louis Mo, 1983.
[2] He Yaqun et al., Aviation Material Supply, National University of Defense Technology Press, 2001. 2.
[3] He Bujie. Manual of Air Material Supply Inventory Model. Blue Sky Publishing House, 1992. 3
[4] Zhang Wenxiu, Wu Weizhi, Liang Jiye, Li Deyu. Rough Set Theory and Method. Beijing: Science Press, 2001.
[5] Deng Julong, Grey Control System, Huazhong Institute of Technology Press, 1986, 348-374.
[6] Liu Xiaomei, Zhou Gang. Construction of unbiased non-homogeneous grey model based on precise integration method, Control and Decision, 2022, 37 (11): 3058-3064.

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