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Economic order quantity with imperfect quality, destructive testing acceptance sampling and inspection errors

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  • Muhammad Al-Salamah

Abstract

This paper proposes another realistic application of the economic order quantity EOQ model. Researchers have studied EOQ models that assume the received lot contains items that are perfect and imperfect. All models in the literature assume that the received lot goes through a 100% inspection to separate acceptable items from the non-acceptable items. This paper argues that 100% inspection is not always a cheap option to the buyer; instead, the received lot is subjected to an acceptance sampling plan before the lot can be accepted. In addition, for real life cases, destructive testing of items in the acceptance sample must be performed to test the acceptability of the items in the lot, as in packaged food and electrical wires. This paper will develop an EOQ model when items are of perfect and imperfect quality and a single acceptance sampling plan with destructive testing and inspection errors is adopted. It is assumed that when the lot is rejected, items in the rejected lot are sold at a secondary market at a reduced price and the buyer will place another order. It is also assumed that there are inspection errors.

Suggested Citation

  • Muhammad Al-Salamah, 2011. "Economic order quantity with imperfect quality, destructive testing acceptance sampling and inspection errors," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 1(2), pages 1-3.
  • Handle: RePEc:spt:admaec:v:1:y:2011:i:2:f:1_2_3
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    Cited by:

    1. Hlioui, Rached & Gharbi, Ali & Hajji, Adnène, 2015. "Replenishment, production and quality control strategies in three-stage supply chain," International Journal of Production Economics, Elsevier, vol. 166(C), pages 90-102.
    2. Naoufel Cheikhrouhou & Biswajit Sarkar & Baishakhi Ganguly & Asif Iqbal Malik & Rafael Batista & Young Hae Lee, 2018. "Optimization of sample size and order size in an inventory model with quality inspection and return of defective items," Annals of Operations Research, Springer, vol. 271(2), pages 445-467, December.

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