Solving multi-objective cell design problem: an evolutionary genetic algorithm approach

L.N. Pattanaik, P.K. Jain and N.K. Mehta

International Journal of Manufacturing Technology and Management, 2007, vol. 11, issue 2, 251-273

Abstract: In this research, an evolutionary genetic algorithm based approach is proposed for designing independent machine cells in cellular manufacturing in the presence of operation based alternative process plans for parts. Manufacturing parameters, such as production volume, usage factor for process plans, machine flexibility and cell size and machine-operation compatibility in terms of a normalised rating factor are also considered during cell design. The problem is formulated as a multi-objective optimisation model and solved using a non-dominated sorting genetic algorithm (NSGA). A unique feature of the proposed method is that it takes into account the unequal efficiency of machines in performing different operations. Some new similarity and diversity measures among operations and machines are also proposed during the clustering of machines. The formation of machine cells has been treated as a minimisation of inter-cell traffic while maximising a defined efficiency function. An illustrated problem and comparisons with existing data are given.

Keywords: alternative process plans; cell formation; inter-cellular movement; multi-objective genetic algorithms; process planning; cellular manufacturing; manufacutring cells; machine cells; cell design; machine efficiency; clustering. (search for similar items in EconPapers)
Date: 2007
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