IDEAS home Printed from https://ideas.repec.org/h/spr/oprchp/978-3-540-69995-8_30.html
   My bibliography  Save this book chapter

A Multi-Objective Particle Swarm for a Mixed-Model Assembly Line Sequencing

In: Operations Research Proceedings 2006

Author

Listed:
  • Seyed Mohammed Mirghorbani

    (University of Tehran)

  • Masoud Rabbani

    (University of Tehran)

  • Reza Tavakkoli-Moghaddam

    (University of Tehran)

  • Alireza R. Rahimi-Vahed

    (University of Tehran)

Abstract

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management. In this paper, three goals are considered for minimization; That is, total utility work, total production rate variation, and total setup cost. A hybrid multi-objective algorithm based on Particle Swarm Optimization (PSO) and Tabu Search (TS) is devised to solve the problem. The algorithm is then compared with three prominent multi-objective Genetic Algorithms and the results show the superiority of the proposed algorithm.

Suggested Citation

  • Seyed Mohammed Mirghorbani & Masoud Rabbani & Reza Tavakkoli-Moghaddam & Alireza R. Rahimi-Vahed, 2007. "A Multi-Objective Particle Swarm for a Mixed-Model Assembly Line Sequencing," Operations Research Proceedings, in: Karl-Heinz Waldmann & Ulrike M. Stocker (ed.), Operations Research Proceedings 2006, pages 181-186, Springer.
  • Handle: RePEc:spr:oprchp:978-3-540-69995-8_30
    DOI: 10.1007/978-3-540-69995-8_30
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Masood Fathi & María Jesús à lvarez & Victoria Rodríguez, 2016. "A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(2), pages 145-169.
    2. Ioanna Makarouni & John Psarras & Eleftherios Siskos, 2015. "Interactive bicriterion decision support for a large scale industrial scheduling system," Annals of Operations Research, Springer, vol. 227(1), pages 45-61, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:oprchp:978-3-540-69995-8_30. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.