Decision-making Method for Formulating Spares Reserve Scheme Based on Deep Neural Network

doi:10.16182/j.issn1004731x.joss.19-FZ0390

Abstract

Abstract: Spare parts classification is important for spare parts storage and is a key part of spare parts decision-making activities. This paper analyzes the factors affecting the reserve scheme of wartime spares. Then by analyzing the inherent attributes of wartime spares, two methods of spare parts classification are proposed to determine the variety and quantity of wartime spares based on deep neural network: (1) Ranks wartime spares according to their importance. A relatively simple deep neural network is used to analyze every attribute of the wartime spares in turn; (2) Inputs all the attributes of wartime spares into a relatively complex deep neural network to make the decision. The experimental results show the advantages of the two methods in terms of efficiency and accuracy for formulating the reserve scheme of wartime spares.

Key words: spare parts, reserve scheme, deep neural network, attribute analysis, decision making

CLC Number:

TP182

Zhang Yunjing, Tang Guangming, Xu Xiaoyu. Decision-making Method for Formulating Spares Reserve Scheme Based on Deep Neural Network[J]. Journal of System Simulation, 2019, 31(11): 2238-2246.

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