Study of Equipment Material Demand Prediction Method Based on Time-continuous Grey Markov Model

ZHANG Lei;LI Shi-min;ZHU Gang

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2017, Vol. 38 ›› Issue (9) : 1862-1866. DOI: 10.3969/j.issn.1000-1093.2017.09.025
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Study of Equipment Material Demand Prediction Method Based on Time-continuous Grey Markov Model

  • ZHANG Lei1, LI Shi-min2, ZHU Gang3
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Abstract

The time continuous and state discrete grey Markov model is used to predict the demand of equipment maintenance materials. The state intervals are set according to the changing amplitude and distribution of consumed maintenance materials. The one-step Markov transition matrix is calculated by states transition. Kolmogorov differential equations are used to solve the time functions of state probabilities and establish the prediction equations of state probabilities. The grey positioning coefficient is determined from the probability values of predicted states. The case analysis shows that the prediction accuracy of the improved grey Markov model ishigher than those of GM(1, 1) model, traditional grey Markov residual error correction model and grey Markov chain model. Its validity and practicability were proven during the prediction of equipment material demand.Key

Key words

ordnancescienceandtechnology / Markovmodel / greypredictionmodel / Kolmogorovdifferentialeauation / predictionaccuracy

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ZHANG Lei, LI Shi-min, ZHU Gang. Study of Equipment Material Demand Prediction Method Based on Time-continuous Grey Markov Model. Acta Armamentarii. 2017, 38(9): 1862-1866 https://doi.org/10.3969/j.issn.1000-1093.2017.09.025

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第38卷
第9期2017年9月兵工学报ACTA
ARMAMENTARIIVol.38No.9Sep.2017

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