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An inventory model for deteriorating items with partial backlogging using linear demand in fuzzy environment

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  • Neeraj Kumar
  • Sanjey Kumar

Abstract

This model presents the invention scheduling difficulty in industry with different operational constraints, as well as strategic plan of the business, revenue purpose, and limit on finishing. In this article, we have studied an inventory model for deteriorating items with shortages under partially backlogging. The aim of present study is to minimize the total cost function in fuzzy environment. Graded mean representation, signed distance, and centroid methods are used to defuzzify the total cost function over the planning horizon. Further, all costs are defuzzified with the help of triangular fuzzy numbers. Finally, sensitivity analysis is also given to show the effect of the costs.

Suggested Citation

  • Neeraj Kumar & Sanjey Kumar, 2017. "An inventory model for deteriorating items with partial backlogging using linear demand in fuzzy environment," Cogent Business & Management, Taylor & Francis Journals, vol. 4(1), pages 1307687-130, January.
    • Handle: RePEc:taf:oabmxx:v:4:y:2017:i:1:p:1307687
    DOI: 10.1080/23311975.2017.1307687
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    References listed on IDEAS

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    1. Chang, San-Chyi & Yao, Jing-Shing & Lee, Huey-Ming, 1998. "Economic reorder point for fuzzy backorder quantity," European Journal of Operational Research, Elsevier, vol. 109(1), pages 183-202, August.
    2. 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.
    3. Debashis Dutta & Pavan Kumar, 2015. "A partial backlogging inventory model for deteriorating items with time-varying demand and holding cost," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 7(3), pages 281-296.
    4. Chang, Hung-Chi & Yao, Jing-Shing & Ouyang, Liang-Yuh, 2006. "Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand," European Journal of Operational Research, Elsevier, vol. 169(1), pages 65-80, February.
    5. Vujosevic, Mirko & Petrovic, Dobrila & Petrovic, Radivoj, 1996. "EOQ formula when inventory cost is fuzzy," International Journal of Production Economics, Elsevier, vol. 45(1-3), pages 499-504, August.
    6. R. Uthayakumar & M. Valliathal, 2011. "Fuzzy Economic Production Quantity Model for Weibull Deteriorating Items with Ramp Type of Demand," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 2(3), pages 55-90, July.
    7. Wee, H.M. & Yu, Jonas & Chen, M.C., 2007. "Optimal inventory model for items with imperfect quality and shortage backordering," Omega, Elsevier, vol. 35(1), pages 7-11, February.
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    Cited by:

    1. Sachin Kumar Rana & Amit Kumar, 2024. "Two-Warehouse Inventory Control for Deteriorating Items Using Hybrid and Stock-Dependent Demand with Partial Backlogging," SN Operations Research Forum, Springer, vol. 5(4), pages 1-18, December.

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