IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v118y2009i1p26-33.html
   My bibliography  Save this article

An economic order quantity model for an imperfect production process with entropy cost

Author

Listed:
  • Jaber, M.Y.
  • Bonney, M.
  • Moualek, I.

Abstract

Among the assumptions of the classical economic order quantity (EOQ) model is that all units that are purchased (or produced) are of perfect quality. However, this is frequently unrealistic since production processes deteriorate resulting in the production of defective products requiring rework. Some recent studies suggest that production systems performance might be improved by applying the first and second laws of thermodynamics to reduce system entropy (or disorder). This paper applies the concept of entropy cost to extend the classical EOQ model under the assumptions of perfect and imperfect quality. Mathematical models are developed and numerical examples illustrating the solution procedure are provided. Accounting for entropy cost suggests that order quantities should be larger than the figures derived from the classical EOQ model.

Suggested Citation

  • Jaber, M.Y. & Bonney, M. & Moualek, I., 2009. "An economic order quantity model for an imperfect production process with entropy cost," International Journal of Production Economics, Elsevier, vol. 118(1), pages 26-33, March.
  • Handle: RePEc:eee:proeco:v:118:y:2009:i:1:p:26-33
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(08)00243-0
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    as
    1. Chyr, Fuchiao & Lin, Tsong Ming & Ho, Chin-Fu, 1990. "Comparison between just-in-time and EOQ system," Engineering Costs and Production Economics, Elsevier, vol. 18(3), pages 233-240, January.
    2. Agnihothri, Saligrama R. & Kenett, Ron S., 1995. "The impact of defects on a process with rework," European Journal of Operational Research, Elsevier, vol. 80(2), pages 308-327, January.
    3. Geren, Necdet & Redford, Alan, 1999. "Cost and performance analysis of a robotic rework cell," International Journal of Production Economics, Elsevier, vol. 58(2), pages 159-172, January.
    4. Chand, Suresh, 1989. "Lot sizes and setup frequency with learning in setups and process quality," European Journal of Operational Research, Elsevier, vol. 42(2), pages 190-202, September.
    5. Jaber, Mohamad Y. & Rosen, Marc A., 2008. "The economic order quantity repair and waste disposal model with entropy cost," European Journal of Operational Research, Elsevier, vol. 188(1), pages 109-120, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Erjavec, J. & Gradisar, M. & Trkman, P., 2012. "Assessment of stock size to minimize cutting stock production costs," International Journal of Production Economics, Elsevier, vol. 135(1), pages 170-176.
    2. Yoo, Seung Ho & Kim, DaeSoo & Park, Myung-Sub, 2009. "Economic production quantity model with imperfect-quality items, two-way imperfect inspection and sales return," International Journal of Production Economics, Elsevier, vol. 121(1), pages 255-265, September.
    3. repec:spr:annopr:v:261:y:2018:i:1:d:10.1007_s10479-017-2563-7 is not listed on IDEAS
    4. Paul, Sanjoy Kumar & Sarker, Ruhul & Essam, Daryl, 2014. "Managing real-time demand fluctuation under a supplier–retailer coordinated system," International Journal of Production Economics, Elsevier, vol. 158(C), pages 231-243.
    5. Björk, Kaj-Mikael, 2012. "A multi-item fuzzy economic production quantity problem with a finite production rate," International Journal of Production Economics, Elsevier, vol. 135(2), pages 702-707.
    6. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.

    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:eee:proeco:v:118:y:2009:i:1:p:26-33. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.