IDEAS home Printed from
   My bibliography  Save this paper

Sequencing mixed-model assembly lines to minimise the number of work overload situations


  • Nils Boysen

    () (School of Economics and Business Administration, Friedrich-Schiller-University Jena)

  • Mirko Kiel
  • Armin Scholl

    () (School of Economics and Business Administration, Friedrich-Schiller-University Jena)


The mixed-model sequencing problem is to sequence different product models launched down an assembly line, so that work overload at the stations induced by direct succession of multiple labour-intensive models is avoided. As a concept of clearing overload situations, especially applied by Western automobile producers, a team of cross-trained utility workers stands by to support the regular workforce. Existing research assumes that regular and utility workers assemble side-by-side in an overload situation, so that the processing speed is doubled and the workpiece can be finished inside a station's boundaries. However, in many real-world assembly lines the application of utility workers is organised completely differently. Whenever it is foreseeable that a work overload will occur in a production cycle, a utility worker takes over to exclusively execute work, whereas the regular worker omits the respective cycle and starts processing the successive workpiece as soon as possible. This study investigates this more realistic sequencing problem and presents a binary linear program along with a complexity proof. Different exact and heuristic solution procedures are then introduced and tested. Additional experiments show that the new model is preferable from an economic point of view whenever utility work causes considerable setup activities, for example walking to the respective station.

Suggested Citation

  • Nils Boysen & Mirko Kiel & Armin Scholl, 2010. "Sequencing mixed-model assembly lines to minimise the number of work overload situations," Jena Research Papers in Business and Economics - Working and Discussion Papers (Expired!) 05/2010, Friedrich-Schiller-University Jena, School of Economics and Business Administration.
  • Handle: RePEc:jen:jenjbe:2010-05

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Bautista, J. & Companys, R. & Corominas, A., 1996. "Heuristics and exact algorithms for solving the Monden problem," European Journal of Operational Research, Elsevier, vol. 88(1), pages 101-113, January.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Mixed-Model Assembly Lines; Sequencing; Utility Work; Branch and Bound; Tabu Search;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:jen:jenjbe:2010-05. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.