Skip to main content

Multi-objective Particle Swarm Optimization for Sequencing and Scheduling a Cellular Manufacturing System

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 93))

Abstract

This paper presents a group scheduling problem for manufacturing cells, in which parts may visit different cells. By addressing intra-cell scheduling, the sequence of parts within manufacturing cells is determined; however, in inter-cell scheduling the sequence of cells is obtained. We design a new mathematical model for a multi-objective group scheduling problem in a cellular manufacturing system (CMS) with respect to bi-objectives minimizing the makespan and intra-cell movement and the tardiness cost. Thus, we develop a meta-heuristic algorithm based on particle swarm optimization (PSO) in order to solve the given problem. The related results confirm the efficiency and the effectiveness of our proposed PSO to provide good solutions, especially for medium and large-sized problems.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lu, L.F., Yuan, J.J.: The Single Machine Batching Problem with Identical Family Setup Times to Minimize Maximum Lateness is Strongly NP-Hard. European Journal of Operational Research 177, 1302–1309 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  2. Lin, S.-W., Ying, K.-C., Lee, Z.-J.: Meta-heuristics for Scheduling a Non-permutation Flow Line Manufacturing Cell with Sequence Dependent Family Setup Times. Computers & Operations Research 36(4), 1110–1121 (2009)

    Article  MATH  Google Scholar 

  3. Hitomi, K., Ham, I.: Operations Scheduling For Group Technology Applications. Annals of the CIRP 25(1), 419–422 (1976)

    Google Scholar 

  4. Tavakkoli-Moghaddam, R., Gholipour-Kanani, Y., Cheraghalizadeh, R.: A Genetic and Memetic Algorithm Approach to Sequencing and Scheduling of Cellular Manufacturing Systems. Int. J. of Management Science and Engineering Management 3(2), 119–130 (2008)

    Google Scholar 

  5. Rahimi-Vahed, A.R., Mirghorbani, S.M., Rabbani, M.: A New Particle Swarm Algorithm for a Multi-objective Mixed-model Assembly Line Sequencing Problem. Soft Computing 11, 997–1012 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tavakkoli-Moghaddam, R., Jafari-Zarandini, Y., Gholipour-Kanani, Y. (2010). Multi-objective Particle Swarm Optimization for Sequencing and Scheduling a Cellular Manufacturing System. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14831-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14830-9

  • Online ISBN: 978-3-642-14831-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics