IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/1379315.html
   My bibliography  Save this article

Genetic Algorithm for Mixed Integer Nonlinear Bilevel Programming and Applications in Product Family Design

Author

Listed:
  • Chenlu Miao
  • Gang Du
  • Yi Xia
  • Danping Wang

Abstract

Many leader-follower relationships exist in product family design engineering problems. We use bilevel programming (BLP) to reflect the leader-follower relationship and describe such problems. Product family design problems have unique characteristics; thus, mixed integer nonlinear BLP (MINLBLP), which has both continuous and discrete variables and multiple independent lower-level problems, is widely used in product family optimization. However, BLP is difficult in theory and is an NP-hard problem. Consequently, using traditional methods to solve such problems is difficult. Genetic algorithms (GAs) have great value in solving BLP problems, and many studies have designed GAs to solve BLP problems; however, such GAs are typically designed for special cases that do not involve MINLBLP with one or multiple followers. Therefore, we propose a bilevel GA to solve these particular MINLBLP problems, which are widely used in product family problems. We give numerical examples to demonstrate the effectiveness of the proposed algorithm. In addition, a reducer family case study is examined to demonstrate practical applications of the proposed BLGA.

Suggested Citation

  • Chenlu Miao & Gang Du & Yi Xia & Danping Wang, 2016. "Genetic Algorithm for Mixed Integer Nonlinear Bilevel Programming and Applications in Product Family Design," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, September.
    • Handle: RePEc:hin:jnlmpe:1379315
    DOI: 10.1155/2016/1379315
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2016/1379315.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2016/1379315.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2016/1379315?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:1379315. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.