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Enhancing group method of data handling type modeling for nonlinear systems in inventory control

Published online by Cambridge University Press:  13 June 2013

Maryam Pournasir ,
Md. Jahangir Alam  and
G. Marthandan
Maryam Pournasir*
Affiliation:
Faculty of Information Technology, Multimedia University, Cyberjaya, Malaysia
Md. Jahangir Alam
Affiliation:
Faculty of Management, Multimedia University, Cyberjaya, Malaysia
G. Marthandan
Affiliation:
Faculty of Information Technology, Multimedia University, Cyberjaya, Malaysia
*
Reprint requests to: Maryam Pournasir, Faculty of Information Technology, Multimedia University, Cyberjaya, Malaysia. E-mail: Pournasir.m@gamil.com
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Abstract

Appropriate topology and coefficients have a great impact on their models' performances. This paper proposes an enhanced group method of data handling (GMDH) algorithm using a modified Levenberg–Marquardt (LM) method. In this work, evolutionary algorithm and LM techniques are deployed simultaneously for optimal design of both connectivity configuration and associated coefficients of each candidate solution in the evolving population combination. However, use of singular value decomposition technique with LM is considered as a novel method to overcome the problem of initial guess, which is presented for the first time in this research. In order to illustrate the benefits of the proposed algorithm, it has been applied in a Malaysian manufacturing company for forecasting in inventory control. Moreover, a comparison between the basic GMDH algorithm and the enhanced GMDH algorithm is made, and the results show that the accuracy of the proposed method is considerably high with reasonable reduction in processing time. Therefore, the enhanced GMDH method can be used to replace old techniques in an inventory control system to generate a structure when it is applied to a Kanban setting in the just in time system.

Keywords

Group Method of Data HandlingKanbanLevenberg–Marquard MethodSingular Value Decomposition
Type
Regular Articles
Information
AI EDAM , Volume 27 , Issue 4 , November 2013 , pp. 377 - 385
Copyright
Copyright © Cambridge University Press 2013 

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