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A novel intelligent particle swarm optimization algorithm for solving cell formation problem

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Abstract

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.

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Acknowledgements

The authors are grateful to the managerial team of the case company for providing the related data for our analysis.

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Authors and Affiliations

  1. Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Vahid Mahmoodian, Armin Jabbarzadeh & Farnaz Barzinpour

  2. Department of Industrial Engineering, University of Tabriz, Tabriz, Iran

    Hassan Rezazadeh

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  1. Vahid Mahmoodian
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  2. Armin Jabbarzadeh
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  3. Hassan Rezazadeh
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  4. Farnaz Barzinpour
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Correspondence to Armin Jabbarzadeh.

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Mahmoodian, V., Jabbarzadeh, A., Rezazadeh, H. et al. A novel intelligent particle swarm optimization algorithm for solving cell formation problem. Neural Comput & Applic 31 (Suppl 2), 801–815 (2019). https://doi.org/10.1007/s00521-017-3020-x

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