Manufacturing cell formation using a new self-organizing neural network

Authors:
Fernando Guerrero;Sebastian Lozano;Kate A. Smith;David Canca;Terence Kwok
Affiliations:
Escuela Superior de Ingenieros, University of Seville, Seville, Spain;Escuela Superior de Ingenieros, University of Seville, Seville, Spain;School of Business Systems, Monash University, Clayton, Australia;Escuela Superior de Ingenieros, University of Seville, Seville, Spain;School of Business Systems, Monash University, Clayton, Australia
Venue:
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Year:
2002

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Abstract

Cellular manufacturing consists of grouping similar machines in cells and dedicating each of them to process a family of similar part types. In this paper, grouping parts into families and machines into cells is done in two steps: first, part families are formed and then machines are assigned. In phase one, weighted similarity coefficients are computed and parts are clustered using a new self-organizing neural network. In phase two, a linear network flow model is used to assign machines to families. To test the proposed approach, different problems from the literature have been solved. As benchmarks we have used a Maximum Spanning Tree heuristic.