IDEAS home Printed from https://ideas.repec.org/a/taf/tjmaxx/v8y2021i1p134-170.html
   My bibliography  Save this article

EPQ models with bivariate random imperfect proportions and learning-dependent production and demand rates

Author

Listed:
  • S. Ganesan
  • R. Uthayakumar

Abstract

In this paper, three production inventory models are constructed for an imperfect manufacturing system by considering a warm-up production run, shortages during the hybrid maintenance period, and the rework of imperfect items. The proportions of imperfect items produced during the warm-up and regular production runs are random and they are represented using a bivariate random variable. The shortage quantity is partially backordered and the supply of backorder quantity is planned simultaneously with regular demand satisfaction. The learning models are designed to accommodate the different learning capabilities of workers in unit production time during warm-up and regular production periods. The production and demand rates of these models are made dependent on the learning exponents. As the resulting models are highly nonlinear in the decision variable, they are optimized using a genetic algorithm. The models are illustrated using numerical examples and sensitivity studies are performed to find the influence of the key parameters.

Suggested Citation

  • S. Ganesan & R. Uthayakumar, 2021. "EPQ models with bivariate random imperfect proportions and learning-dependent production and demand rates," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(1), pages 134-170, January.
  • Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:134-170
    DOI: 10.1080/23270012.2020.1818320
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23270012.2020.1818320
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/23270012.2020.1818320?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
    ---><---

    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. S. Ganesan & R. Uthayakumar, 2023. "An EPQ model for a single-machine multiproduct imperfect production system using a hybrid logarithmic barrier method," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1121-1152, September.
    2. Erfan Nobil & Leopoldo Eduardo Cárdenas-Barrón & Imelda de Jesús Loera-Hernández & Neale R. Smith & Gerardo Treviño-Garza & Armando Céspedes-Mota & Amir Hossein Nobil, 2023. "Sustainability Economic Production Quantity with Warm-Up Function for a Defective Production System," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    3. Erfan Nobil & Leopoldo Eduardo Cárdenas-Barrón & Dagoberto Garza-Núñez & Gerardo Treviño-Garza & Armando Céspedes-Mota & Imelda de Jesús Loera-Hernández & Neale R. Smith & Amir Hossein Nobil, 2023. "Machine Downtime Effect on the Warm-Up Period in an Economic Production Quantity Problem," Mathematics, MDPI, vol. 11(7), pages 1-23, April.

    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:taf:tjmaxx:v:8:y:2021:i:1:p:134-170. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjma .

    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.