IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i7d10.1007_s00180-024-01581-3.html
   My bibliography  Save this article

Assessing the economic-statistical performance of variable acceptance sampling plans based on loss function

Author

Listed:
  • Samrad Jafarian-Namin

    (Alzahra University)

  • Parviz Fattahi

    (Alzahra University)

  • Ali Salmasnia

    (University of Qom)

Abstract

Acceptance sampling plans (ASPs) for attributes are sometimes misapplied to normal quality characteristics. When inspection costs and quality levels are high, using variable ASPs (VASPs) can be preferable. Among developed approaches to design ASPs, few studies have incorporated losses into the cost objective function. Their limited attention, such as focusing on limited random scenarios, considering only the activation of one specification limit, failing to compare VASPs with military standards still in use, and relying on time-consuming solution procedures, motivated us to utilize the advantages of loss-based economic-statistical design, evaluate four VASPs and two military standards, and presenting detailed results. Additionally, we develop the first Particle swarm optimization (PSO)-based solution procedure for designing VASPs. Numerical and real case studies, which consider the activation of lower and upper specification limits, demonstrate the superior performance of (1) the repetitive group sampling plan, (2) MIL-STD-414 over MIL-STD-105E, and (3) PSO compared to other approaches.

Suggested Citation

  • Samrad Jafarian-Namin & Parviz Fattahi & Ali Salmasnia, 2025. "Assessing the economic-statistical performance of variable acceptance sampling plans based on loss function," Computational Statistics, Springer, vol. 40(7), pages 3665-3713, September.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:7:d:10.1007_s00180-024-01581-3
    DOI: 10.1007/s00180-024-01581-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01581-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-024-01581-3?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.

    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:spr:compst:v:40:y:2025:i:7:d:10.1007_s00180-024-01581-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.