Application of gray prediction and linear programming model in economic management

Application of gray prediction and linear programming model in economic management

Shuli Song

Heilongjiang Bayi Agricultural University, Daqing 163319, China

Corresponding Author Email: 
454673843@qq.com
Page: 
46-50
|
DOI: 
https://doi.org/10.18280/mmep.050107
Received: 
15 October 2017
|
Revised: 
3 January 2018
|
Accepted: 
12 January 2018
|
Available online: 
31 March 2018
| Citation

OPEN ACCESS

Abstract: 

At present, the gray system theory has enjoyed immense popularity in the field of economy and management. From gray optimization, gray control to gray prediction, the results of the theory have been paid more and more attention and been applied extensively in the economic development and enterprise management. Aiming at constructing a gray linear programming model based on gray prediction and applying the model to enterprise operation and management, this paper forecasts the future technical progress of the enterprise through gray prediction and verifies the accuracy of the prediction. It is proved that the prediction has a high accuracy, indicating that the gray prediction model is applicable to the forecast of technical level. Besides, this paper establishes a linear programming model to analyze the investment income of different projects in an enterprise, thereby providing the basis for managers to make decisions.

Keywords: 

gray prediction, linear programming model, technical progress, investment benefit

1. Introduction
2. Construction of Gray Prediction Calculation Model
3. Establishment and Application of Linear Programming Model Based on Gray Prediction
4. Conclusions
  References

[1] Chen Z, Chen Q, Chen W, Wang Y. (2004). Grey linear programming. Kybernetes 33(2): 238-246. 

[2] Liang RH. (1997). Application of grey linear programming to short-term hydro scheduling. Electric Power Systems Research 41(3): 159-165. https://doi.org/10.1016/S0378-7796(96)01128-5

[3] Li QX. (2007). The cover solution of grey linear programming. Journal of Grey System 19(4): 309-320. 

[4] Tang W, Rao C, Li L. (2011). Grey linear programming problem of commodities' scheduling in large-scale emergency. Journal of Grey System 23(3): 281-290. 

[5] Wang Z. (2013). Application of grey linear control theory for price regulation in china's real estate market. Kybernetes 42(3): 413-422. https://doi.org/10.1108/03684921311323662

[6] Xiao X, Lu Y. (2011). Grey linear regression model and its application. Kybernetes 41(5-6): 177-181. https://doi.org/10.1109/GSIS.2011.6044045

[7] Lang L, Chen JH, Zheng HL. (2012). Application of fuzzy grey predictability linear programming in capacity allocation of mine. Journal of Central South University 43(2): 611-619.

[8] Su CH, Liu SF. (2008). Asymptotic stability of grey stochastic linear delay systems. Kongzhi Yu Juece/control & Decision 23(5): 571-574, 580.

[9] Grey A, Sekar A. (2008). Unified solution of security-constrained unit commitment problem using a linear programming methodology. Iet Generation Transmission & Distribution 2(6): 856-867. https://doi.org/10.1049/iet-gtd:20070367

[10] Guo P, Huang GH, Li YP. (2010). An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties. Advances in Water Resources 33(1): 81-91. https://doi.org/10.1016/j.advwatres.2009.10.009

[11] Liu Y, Huang G, Cai Y, Dong C. (2011). An inexact mix-integer two-stage linear programming model for supporting the management of a low-carbon energy system in china. Energies 4(10): 1657-1686. https://doi.org/10.3390/en4101657

[12] Fan YR. (2012). A robust two-step method for solving interval linear programming problems within an environmental management context. Journal of Environmental Informatics 19(1): 1-9. 

[13] Li QX. (2014). The definition system and computational rule of grey determinant and its application to n grey equations with n grey linear equations. Grey Systems 2(1): 359 - 384. https://doi.org/10.1108/20439371211273258

[14] Cao DL, He CH, Li XF. (2008). Prediction of gdp in henan province based on a grey linear regression combined model. Journal of Henan Agricultural University 42(4): 469-472.

[15] Kose E, Forrest YL. (2015). N-person grey game. Kybernetes 44(2): 271-282. https://doi.org/10.1016/10.1108/K-04-2014-0073

[16] Ren A. (2015). A novel method for solving the fully fuzzy bilevel linear programming problem. Mathematical Problems in Engineering 2015(2): 1-11. https://doi.org/10.1155/2015/280380

[17] Xu B, Li N, Bai F. (2007). An interactive compensatory fuzzy algorithm for grey decentralized bi-level drift-type linear programming model. Systems Engineering 25(11): 91-96. 

[18] Mosher SW, Densmore JD. (2005). Stability and monotonicity conditions for linear, grey, 0-d implicit monte carlo calculations. Transactions of the American Nuclear Society 93: 520-522. 

[19] Su CH, Liu SF. (2008). Robust stability of grey neutral linear time-delay systems. Acta Mathematicae Applicatae Sinica 31(3): 520-527.