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A tabu search algorithm for the cell formation problem with part machine sequencing

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posted on 19.03.2015, 14:39 by Grammatoula PapaioannouGrammatoula Papaioannou, John M. Wilson
This paper presents extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously and part machine utilisation is considered. More specifically, an integration of inter-cell movements of parts and machine set-up costs within the objective function, and also a combination of machine set-up costs associated with parts revisiting a cell when the part machine operation sequence is taken into account are examined and an enhanced model is formulated. Based upon this model's requirements, an initial three stage approach is proposed and a tabu search iterative procedure is designed to produce a solution. The initial approach consists of the allocation of machines to cells, the allocation of parts to machines in cells and the evaluation of the objective function's value. Special care has been taken when allocating parts to machine cells as part machine operation sequence is preserved making the system more complex but more realistic. The proposed tabu search algorithm integrates short term memory and an overall iterative searching strategy where two move types, single and exchange, are considered. Computational experiments verified both the algorithm's robustness where promising solutions in reasonably short computational effort are produced and also the algorithm's effectiveness for large scale data sets.



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Foundations of Computing and Decision Sciences






97 - 127


PAPAIOANNOU, G. and WILSON, J.M., 2012. A tabu search algorithm for the cell formation problem with part machine sequencing. Foundations of Computing and Decision Sciences, 37 (2), pp. 97 - 127.


De Gruyter Open / © The Authors


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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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This article was published in the journal Foundations of Computing and Decision Sciences [De Gruyter Open / © The Authors]. It is also available at: http://dx.doi.org/10.2478/v10209-011-0008-7