<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The demand for spare parts of weapons and equipment is time-varying and random. It is difficult to predict the demand for spare parts. Therefore, on the basis of gray GM(1,1), a state transition probability matrix based on improved state division is used to establish a demand forecast model for weapon equipment and spare parts. The model not only considers the characteristics of the GM(1,1) model’s strong handling of monotonic sequences, but also extracts the characteristics of random fluctuation response of data through the transformation of the state transition probability matrix, avoiding the phenomenon of the worst prediction results when the maximum probability state is not the actual state. It is proved through experiments that the prediction result based on the improved gray-Markov model is superior to the traditional model and the classic gray-Markov prediction model, and the accuracy of the improved model is about 1.46 times higher than that of the gray model.</p></abstract>

The demand for spare parts of weapons and equipment is time-varying and random. It is difficult to predict the demand for spare parts. Therefore, on the basis of gray GM(1,1), a state transition probability matrix based on improved state division is used to establish a demand forecast model for weapon equipment and spare parts. The model not only considers the characteristics of the GM(1,1) model’s strong handling of monotonic sequences, but also extracts the characteristics of random fluctuation response of data through the transformation of the state transition probability matrix, avoiding the phenomenon of the worst prediction results when the maximum probability state is not the actual state. It is proved through experiments that the prediction result based on the improved gray-Markov model is superior to the traditional model and the classic gray-Markov prediction model, and the accuracy of the improved model is about 1.46 times higher than that of the gray model.

Demand Forecast of Weapon Equipment Spare Parts Based on Improved Gray-Markov Model

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The demand for spare parts of weapons and equipment is time-varying and random. It is difficult to predict the demand for spare parts. Therefore, on the basis...

Raw data	Model calculation data	Raw data	Model calculation data
20.0000	20.0000	10.0000	10.5184
14.0000	15.8176	11.0000	10.4467
18.0000	16.9127	6.0000	6.1321
9.0000	10.6324	5.0000	6.5528
Predict three numbers	3.4219	2.4896	1.2732

Serial number	1	2	3	4	5	6	7	8
Number of spare parts	20	14	18	9	10	11	6	5

Number of states	Model mean error	Number of states	Model mean error
2	15.9536%	6	Undesirable
3	13.7272%	7	Undesirable
4	11.5191%	8	Undesirable
5	Undesirable	—	—

GM(1,1)model	Number of states	Classic model	Improve the model
	2	15.9536%	15.9536%
	3	13.7272%	13.7272%
16.8686%	4	Undesirable	11.5191%
	5	Undesirable	Undesirable
	6	Undesirable	Undesirable

Demand Forecast of Weapon Equipment Spare Parts Based on Improved Gray-Markov Model

Pubblicato online: 14 ott 2020

Pagine: 47 - 56

DOI: https://doi.org/10.21307/ijanmc-2020-027

Parole chiave
Grey Theory, Markov Model, Spare Parts Forecast

© 2020 Ou Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

FORECAST RESULTS

HISTORICAL DATA OF SPARE PARTS CONSUMPTION OF A CERTAIN EQUIPMENT

MODEL AVERAGE ERROR UNDER DIFFERENT STATE DIVISION

FORECAST ACCURACY OBTAINED BY USING DIFFERENT MODELS

Demand Forecast of Weapon Equipment Spare Parts Based on Improved Gray-Markov Model

Pubblicato online: 14 ott 2020

Pagine: 47 - 56

DOI: https://doi.org/10.21307/ijanmc-2020-027

Parole chiaveGrey Theory, Markov Model, Spare Parts Forecast

© 2020 Ou Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

FORECAST RESULTS

HISTORICAL DATA OF SPARE PARTS CONSUMPTION OF A CERTAIN EQUIPMENT

MODEL AVERAGE ERROR UNDER DIFFERENT STATE DIVISION

FORECAST ACCURACY OBTAINED BY USING DIFFERENT MODELS

Parole chiave
Grey Theory, Markov Model, Spare Parts Forecast