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Economic order quantity model for items with imperfect quality with learning in inspection

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  • Khan, M.
  • Jaber, M.Y.
  • Wahab, M.I.M.

Abstract

The economic order quantity (EOQ) model is the simplest and earliest inventory model in the literature. Its simple mathematics is attributed to its assumptions, which are rarely met. Salameh and Jaber [2000. Economic production quantity model for items with imperfect quality. Int. J. Prod. Econ. 64, 59-64] addressed one of these assumptions that items received or produced are not of perfect quality. This paper extends Salameh and Jaber's work for the case where there is learning in inspection. The model is realistic in that it considers situations of lost sales and backorders. Mathematical models are developed with numerical examples provided and results discussed for the cases of (i) partial transfer of learning, (ii) total transfer of learning, and (iii) no transfer of learning.

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  • Khan, M. & Jaber, M.Y. & Wahab, M.I.M., 2010. "Economic order quantity model for items with imperfect quality with learning in inspection," International Journal of Production Economics, Elsevier, vol. 124(1), pages 87-96, March.
  • Handle: RePEc:eee:proeco:v:124:y:2010:i:1:p:87-96
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    References listed on IDEAS

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    Cited by:

    1. Tapan Kumar Datta, 2017. "Inventory system with defective products and investment opportunity for reducing defective proportion," Operational Research, Springer, vol. 17(1), pages 297-312, April.
    2. József Vörös, 2013. "Economic order and production quantity models without constraint on the percentage of defective items," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(4), pages 867-885, December.
    3. Prasert Aengchuan & Busaba Phruksaphanrat, 2018. "Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 905-923, April.
    4. Hauck, Zsuzsanna & Vörös, József, 2015. "Lot sizing in case of defective items with investments to increase the speed of quality control," Omega, Elsevier, vol. 52(C), pages 180-189.
    5. Mehmood Khan & Matloub Hussain & Leopoldo Eduardo Cárdenas-Barrón, 2017. "Learning and screening errors in an EPQ inventory model for supply chains with stochastic lead time demands," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4816-4832, August.
    6. Chayanika Rout & Ravi Shankar Kumar & Debjani Chakraborty & Adrijit Goswami, 2019. "An EPQ model for deteriorating items with imperfect production, inspection errors, rework and shortages : a type-2 fuzzy approach," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 657-688, September.
    7. R. Patro & Mitali M. Nayak & M. Acharya, 2019. "An EOQ model for fuzzy defective rate with allowable proportionate discount," OPSEARCH, Springer;Operational Research Society of India, vol. 56(1), pages 191-215, March.
    8. Tien-Yu Lin & Kuo-Lung Hou, 2015. "An imperfect quality economic order quantity with advanced receiving," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 535-551, July.
    9. Ivan Darma Wangsa & Hui Ming Wee & Shih-Hsien Tseng, 2019. "A coordinated vendor–buyer system considering loss and damage claims, insurance cost and stochastic lead time," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(3), pages 384-398, June.
    10. Soheyl Khalilpourazari & Seyed Hamid Reza Pasandideh, 2020. "Multi-objective optimization of multi-item EOQ model with partial backordering and defective batches and stochastic constraints using MOWCA and MOGWO," Operational Research, Springer, vol. 20(3), pages 1729-1761, September.
    11. Alamri, Adel A. & Syntetos, Aris A., 2018. "Beyond LIFO and FIFO: Exploring an Allocation-In-Fraction-Out (AIFO) policy in a two-warehouse inventory model," International Journal of Production Economics, Elsevier, vol. 206(C), pages 33-45.
    12. Prasert Aengchuan & Busaba Phruksaphanrat, 0. "Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-19.
    13. Khan, M. & Jaber, M.Y. & Guiffrida, A.L. & Zolfaghari, S., 2011. "A review of the extensions of a modified EOQ model for imperfect quality items," International Journal of Production Economics, Elsevier, vol. 132(1), pages 1-12, July.
    14. Lie-Fern Hsu & Jia-Tzer Hsu, 2016. "Economic production quantity (EPQ) models under an imperfect production process with shortages backordered," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(4), pages 852-867, March.
    15. Tai, Allen H. & Xie, Yue & He, Wanhua & Ching, Wai-Ki, 2019. "Joint inspection and inventory control for deteriorating items with random maximum lifetime," International Journal of Production Economics, Elsevier, vol. 207(C), pages 144-162.
    16. Ahmed Abdel-Aleem & Mahmoud A. El-Sharief & Mohsen A. Hassan & Mohamed G. El-Sebaie, 2017. "A surface response optimization model for EPQ system with imperfect production process under rework and shortage," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 735-751, December.

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