Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

Zhi Jie Song; Zan Fu; Han Wang; Gui Bin Hou

doi:10.4028/www.scientific.net/AMM.433-435.545

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 433-435Demand Forecasting Model of Port Critical Spare...

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

Abstract:

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

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Periodical:

Applied Mechanics and Materials (Volumes 433-435)

Pages:

545-549

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.433-435.545

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Online since:

October 2013

Authors:

Zhi Jie Song, Zan Fu, Han Wang, Gui Bin Hou

Keywords:

Demand Forecasting, Least Squares Support Vector Machines (Ls-SVM), Particle Swarm Optimization (PSO), Port Critical Spare Parts

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References

[1] FeiLong Chen, YunChin Chen and JunYuan Kuo: Expert Systems with Applications. Vol. 37 (2010), p.4360 (In Chinese).

Google Scholar

[2] SOMNATH MUKHOPADHYAY, ADRIANO O. SOLIS and RAFAE S. GUTIERREZ: Journal of Forecasting. Vol. 31 (2012), p.725.

Google Scholar

[3] Andrea Bacchettin, Nicola Saccani: Omega. Vol. 40 (2012), p.730.

Google Scholar

[4] Suykens J A K, Vandewalle J: Neural Processing Letters. Vol. 9(1999), p.295.

Google Scholar

[5] Peng Bai, Xibin Zhang, Bin Zhang et al: Support Vector Machine and its Application in Mixed Gas Infrared Spectrum Analysis. Xi'an Electronic Sience &Technology University Press, (2008), p.72(In Chinese).

Google Scholar

[6] Kennedy J, Eberhart R: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. Perth: IEEE Press, Vol. (1995), p. (1948).

Google Scholar

[7] Qianwen Xiang, Yukun Sun, Xiaofu Ji: Chinese Control and Decision Conference, (2011), p.801.

Google Scholar

[8] L. A. Shalabi, Z. Shaaban, and B. Kasasbeh: J Comput Sci, 2, (2006) p.735.

Google Scholar