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Optimal inventory modeling of supply chain system involving quality inspection errors and fuzzy defective rate

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  • S. Priyan

    (Mepco Schlenk Engineering College)

  • P. Manivannan

    (Mepco Schlenk Engineering College)

Abstract

In this paper an integrated vendor–buyer supply chain model is designed for a defective inventory product. We provide a simple mathematical model for determining an optimal vendor–buyer inventory policy by accounting for fuzzy defective rate and quality inspection errors at the buyer’s end. In this study we fuzzify the defective rate to the triangular fuzzy number in the expected total cost of the entire supply chain. We then used the signed distance method to defuzzify the fuzzy total cost of the system and a methodology has been proposed to minimize this cost. We frame an iterative algorithm procedure to achieve the optimal solution of delivery lot-size, the total number of deliveries from the vendor to the buyer in one production batch, and it has been illustrated by way of a numerical example. Also sensitivity analysis are given to demonstrate the performance of the proposed methodology. Our results indicate that the optimal solutions of the fuzzy case slightly fluctuate from the solutions of the crisp case.

Suggested Citation

  • S. Priyan & P. Manivannan, 2017. "Optimal inventory modeling of supply chain system involving quality inspection errors and fuzzy defective rate," OPSEARCH, Springer;Operational Research Society of India, vol. 54(1), pages 21-43, March.
    • Handle: RePEc:spr:opsear:v:54:y:2017:i:1:d:10.1007_s12597-016-0267-4
    DOI: 10.1007/s12597-016-0267-4
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    References listed on IDEAS

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    1. Yao, Jing-Shing & Chiang, Jershan, 2003. "Inventory without backorder with fuzzy total cost and fuzzy storing cost defuzzified by centroid and signed distance," European Journal of Operational Research, Elsevier, vol. 148(2), pages 401-409, July.
    2. Salameh, M. K. & Jaber, M. Y., 2000. "Economic production quantity model for items with imperfect quality," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 59-64, March.
    3. Maddah, Bacel & Jaber, Mohamad Y., 2008. "Economic order quantity for items with imperfect quality: Revisited," International Journal of Production Economics, Elsevier, vol. 112(2), pages 808-815, April.
    4. Wee, Hui Ming & Widyadana, Gede Agus, 2013. "A production model for deteriorating items with stochastic preventive maintenance time and rework process with FIFO rule," Omega, Elsevier, vol. 41(6), pages 941-954.
    5. Goyal, Suresh Kumar & Cardenas-Barron, Leopoldo Eduardo, 2002. "Note on: Economic production quantity model for items with imperfect quality - a practical approach," International Journal of Production Economics, Elsevier, vol. 77(1), pages 85-87, May.
    6. Berthaut, F. & Gharbi, A. & Dhouib, K., 2011. "Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell," Omega, Elsevier, vol. 39(6), pages 642-654, December.
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    Cited by:

    1. Muhammad Tayyab & Biswajit Sarkar & Misbah Ullah, 2018. "Sustainable Lot Size in a Multistage Lean-Green Manufacturing Process under Uncertainty," Mathematics, MDPI, vol. 7(1), pages 1-18, December.
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    3. Uoseph Hamdi Salemi & Esmaile Khorram & Yuancheng Si & Saralees Nadarajah, 2020. "Sensitivity analysis of censoring schemes in progressively type-II right censored order statistics," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 163-189, March.
    4. Sudipta Sinha & Nikunja Mohan Modak & Shib Sankar Sana, 2020. "An entropic order quantity inventory model for quality assessment considering price sensitive demand," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 88-103, March.

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