NDLI: A new model of dynamic cell formation by a neural approach

Content Provider	Springer Nature Link
Author	Saidi Mehrabad, M. Safaei, N.
Copyright Year	2006
Abstract	This paper proposes a nonlinear integer model of cell formation under dynamic conditions. The cell formation (CF) problem is a portion of a cellular manufacturing strategy (CMS), in which the parts and machines are clustered with the aim of minimizing the material handling cost. In most previous research the cell formation problem has always been under static conditions in which cells are formed for a single-period planning horizon where product mix and demand are constant. In contrast, in dynamic conditions, a multi-period planning horizon is considered, where the product mix and demand in each period is different. This occurs in seasonally or monthly production. As a result, the best cell design for one period may not be efficient for subsequent periods. To verify the presented model, different problems have been solved and results are reported. Where the cell formation problem belongs to NP class, the use of a novel approach is necessary. In this research, we apply a neural approach based on mean filed theory for solving the proposed model. In this approach, the network weights are updated by an interaction procedure. The proposed model is solved by LINGO software and an optimum solution is obtained. Comparison of optimum and neural approach solutions shows the efficiency of the presented neural network approach.
Starting Page	1001
Ending Page	1009
Page Count	9
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	33
Issue Number	9-10
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-05-06
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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