Generation of the Storage Costs Function Using Neural Networks

Emilia Campean; Liviu Morar; Dumitru Pop; Stefan Pap

doi:10.4028/www.scientific.net/AMM.245.209

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 245Generation of the Storage Costs Function Using...

Generation of the Storage Costs Function Using Neural Networks

Abstract:

The main purpose of the paper is to develop a neural network application destined to the raw material stock management, as an performant alternative to the classical models of costs stock management. Stocks of goods that manufacturers would classify as raw materials stocks are, in a special sense, goods in early stages of the production process. The testing was made for three companies from the automotive industry, but it could be applied to any kind of Romanian organization.

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Periodical:

Applied Mechanics and Materials (Volume 245)

Pages:

209-214

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.245.209

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Online since:

December 2012

Authors:

Emilia Campean, Liviu Morar, Dumitru Pop, Stefan Pap

Keywords:

Logistic, Neural Network (NN), Production, Stock Costs, Stock Management

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References

[1] http://www.asian-transactions.org/Journals/Vol01Issue04/ATE/ATE-70109048.pdf

Google Scholar

[2] E. Ciupan, A Model for the Management of a Supply Activity, Based on Statistical Data, Quality and Innovation in Engineering and Management, Cluj Napoca, (2011) 249-253.

Google Scholar

[3] Abrudan, I., Cândea, D., Lungu, F., s.a Manual de inginerie economică. Ingineria şi managementul sistemelor de producţie, Cluj-Napoca, Editura Dacia, (2002), ISBN 973-35-1588-4

Google Scholar

[4] http://www.scribd.com/doc/37676010/69/TIPURI-DE-GESTIUNE-A-STOCURILOR

Google Scholar

[5] Campean E.,Itul T.,Tanase I., Pisla A., Workspace Generation for a 2 - DOF Parallel Mechanism using Neural Networks, MTM Robotics, Clerment-Ferrnad, (2012)

DOI: 10.4028/www.scientific.net/amm.162.121

Google Scholar

[6] Simon Haykin, Neural networks and learning machines, Volume 10, Prentice Hall, (2009)

Google Scholar

[7] Sharda, R., 1994. Neural networks for the MS/OR analyst: An application bibliography. Interfaces 24 (2), 116–130

DOI: 10.1287/inte.24.2.116

Google Scholar

[8] Russell S., Norvig, P., Artificial Intelligence: A Modern Approach. Prentice Hall, (2002).

Google Scholar

[9] Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, Neural Network Toolbox™ User's Guide

Google Scholar