MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)
|Modelling and Simulation
|04 March 2020
Predication of life cycle cost of equipment base on unbiased grey Markov models
1 College of Mechanical and Electrical Engineering, Wuhan Donghu University, Wuhan, China
2 College of Ships and Oceanography, Naval University of Engineering, Wuhan, China
3 Department of Power control, Naval Submarine Academy, Qingdao, China
* Corresponding author: email@example.com
Life cycle cost(LCC) is an important content of equipment integrated logistics support. While the LCC includes the whole life cycle of equipment from development, production, service and maintenance to retirement, in order to effectively manage and control the LCC and better develop integrated logistics support, it is necessary to analyze and predict it. The unbiased grey markov model(UGMM) was introduced into the LCC prediction in the paper, in order to check model accuracy, the posterior difference method(PDM) was used, also the influence by the number of state intervals in UGMM on the prediction accuracy is analyzed and studied. The result indicate that UGMM can be used to predict the LCC, also have the highest prediction accuracy comparing with unbiased grey model and grey separating model, and in order to ensure the prediction accuracy, the state interval should be divided according to the number of sequence.
Key words: Life cycle cost / Unbiased grey model / Grey Markov models / Posterior difference method / Predication
© The Authors, published by EDP Sciences, 2020
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