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Neural Computing and Applications

, Volume 31, Supplement 2, pp 801–815 | Cite as

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

  • Vahid Mahmoodian
  • Armin JabbarzadehEmail author
  • Hassan Rezazadeh
  • Farnaz Barzinpour
Original Article
  • 156 Downloads

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.

Keywords

Cellular manufacturing Cell formation problem Neural networks Particle swarm optimization Discrete learning 

Notes

Acknowledgements

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

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Vahid Mahmoodian
    • 1
  • Armin Jabbarzadeh
    • 1
    Email author
  • Hassan Rezazadeh
    • 2
  • Farnaz Barzinpour
    • 1
  1. 1.Department of Industrial EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Department of Industrial EngineeringUniversity of TabrizTabrizIran

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