IDEAS home Printed from https://ideas.repec.org/a/ids/eujine/v8y2014i3p291-324.html
   My bibliography  Save this article

A fuzzified version of the economic production quantity (EPQ) model with backorders and rework for a single-stage system

Author

Listed:
  • Ehsan Shekarian
  • Mohamad Y. Jaber
  • Nima Kazemi
  • Ehsan Ehsani

Abstract

This paper extends a recent work that investigated a single-stage production-inventory model with reworks and planned backorders by fuzzifying its input parameters. The graded mean integration representation (GMIR) method, a useful and effective defuzzification method, is employed to develop a fuzzified total inventory cost function of model of interest. Triangular and trapezoidal fuzzy numbers are used to examine the developed fuzzy model. Later, the optimal policy, including the batch size, the backordering level and total cost, is determined using the classical approach. Furthermore, the derived optimal policies are tested using arbitrary fuzzy numbers. [Received 4 July 2011; Revised 4 November 2011; Revised 3 February 2012; Revised 16 July 2012; Accepted 27 October 2012]

Suggested Citation

  • Ehsan Shekarian & Mohamad Y. Jaber & Nima Kazemi & Ehsan Ehsani, 2014. "A fuzzified version of the economic production quantity (EPQ) model with backorders and rework for a single-stage system," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 8(3), pages 291-324.
    • Handle: RePEc:ids:eujine:v:8:y:2014:i:3:p:291-324
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=60998
    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.

    Citations

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


    Cited by:

    1. Tyrone T. Lin & Shu-Yen Hsu, 2018. "Risk Management for the Optimal Order Quantity by Risk-Averse Suppliers of Food Raw Materials," IJFS, MDPI, vol. 6(4), pages 1-17, December.
    2. Nima Kazemi & Salwa Hanim Abdul-Rashid & Ehsan Shekarian & Eleonora Bottani & Roberto Montanari, 2016. "A fuzzy lot-sizing problem with two-stage composite human learning," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 5010-5025, August.
    3. Ateka Banu & Shyamal Kumar Mondal, 2020. "Analyzing an inventory model with two-level trade credit period including the effect of customers’ credit on the demand function using q-fuzzy number," Operational Research, Springer, vol. 20(3), pages 1559-1587, September.

    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:eujine:v:8:y:2014:i:3:p:291-324. 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=210 .

    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.