IDEAS home Printed from https://ideas.repec.org/a/ids/ijpqma/v33y2021i4p493-516.html
   My bibliography  Save this article

Maximising availability of complex repairable systems and flexible selection of performance parameter using PSO

Author

Listed:
  • Ajay Kumar

Abstract

In this research study, particle swarm optimisation (PSO) has been implemented for availability analysis of a complex repairable industrial system namely mash kettle of an existing brewery plant. PSO algorithm is computationally simple and efficient. The mash kettle system is modelled structurally representing its various constituent elements/components and their interconnections. Probability relationship of different feasible state are generated using the transition diagram. Performance analysis is done to observe the criticality of the constituent elements/components. The particle swarm optimisation algorithm provides a varied and wide range of performance measures of various elements/components for a predetermined optimised value of the availability of the system. PSO algorithm is validated by comparing with genetic algorithm and Markov methods for the results of optimal availability. The consequences of this paper will be beneficial for the maintenance engineer and plant staff to accomplish best attainable system performance/availability and to plan appropriate maintenance strategies.

Suggested Citation

  • Ajay Kumar, 2021. "Maximising availability of complex repairable systems and flexible selection of performance parameter using PSO," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 33(4), pages 493-516.
    • Handle: RePEc:ids:ijpqma:v:33:y:2021:i:4:p:493-516
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=117253
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijpqma:v:33:y:2021:i:4:p:493-516. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=177 .

    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.