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(Q,r) Inventory policies in a fuzzy uncertain supply chain environment

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  • Handfield, Robert
  • Warsing, Don
  • Wu, Xinmin

Abstract

Managers have begun to recognize that effectively managing risks in their business operations plays an important role in successfully managing their inventories. Accordingly, we develop a (Q,r) model based on fuzzy-set representations of various sources of uncertainty in the supply chain. Sources of risk and uncertainty in our model include demand, lead time, supplier yield, and penalty cost. The naturally imprecise nature of these risk factors in managing inventories is represented using triangular fuzzy numbers. In addition, we introduce a human risk attitude factor to quantify the decision maker's attitude toward the risk of stocking out during the replenishment period. The total cost of the inventory system is computed using defuzzification methods built from techniques identified in the literature on fuzzy sets. Finally, we provide numerical examples to compare our fuzzy-set computations with those generated by more traditional models that assume full knowledge of the distributions of the stochastic parameters in the system.

Suggested Citation

  • Handfield, Robert & Warsing, Don & Wu, Xinmin, 2009. "(Q,r) Inventory policies in a fuzzy uncertain supply chain environment," European Journal of Operational Research, Elsevier, vol. 197(2), pages 609-619, September.
  • Handle: RePEc:eee:ejores:v:197:y:2009:i:2:p:609-619
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    References listed on IDEAS

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    1. Petrovic, Dobrila & Roy, Rajat & Petrovic, Radivoj, 1999. "Supply chain modelling using fuzzy sets," International Journal of Production Economics, Elsevier, vol. 59(1-3), pages 443-453, March.
    2. Petrovic, Dobrila & Roy, Rajat & Petrovic, Radivoj, 1998. "Modelling and simulation of a supply chain in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 109(2), pages 299-309, September.
    3. 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.
    4. Xie, Ying & Petrovic, Dobrila & Burnham, Keith, 2006. "A heuristic procedure for the two-level control of serial supply chains under fuzzy customer demand," International Journal of Production Economics, Elsevier, vol. 102(1), pages 37-50, July.
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    Cited by:

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    2. 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.
    3. Liu, Weihua & Wang, Qian & Mao, Qiaomei & Wang, Shuqing & Zhu, Donglei, 2015. "A scheduling model of logistics service supply chain based on the mass customization service and uncertainty of FLSP’s operation time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 189-215.
    4. Zhao, Jing & Tang, Wansheng & Wei, Jie, 2012. "Pricing decision for substitutable products with retail competition in a fuzzy environment," International Journal of Production Economics, Elsevier, vol. 135(1), pages 144-153.
    5. Jie Wei & Jing Zhao, 2016. "Pricing decisions for substitutable products with horizontal and vertical competition in fuzzy environments," Annals of Operations Research, Springer, vol. 242(2), pages 505-528, July.
    6. Zhao, Jing & Tang, Wansheng & Zhao, Ruiqing & Wei, Jie, 2012. "Pricing decisions for substitutable products with a common retailer in fuzzy environments," European Journal of Operational Research, Elsevier, vol. 216(2), pages 409-419.
    7. Fernandes, Rui & Gouveia, Borges & Pinho, Carlos, 2010. "Modeling Overstock," MPRA Paper 25126, University Library of Munich, Germany.
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    9. Wong, Bo K. & Lai, Vincent S., 2011. "A survey of the application of fuzzy set theory in production and operations management: 1998-2009," International Journal of Production Economics, Elsevier, vol. 129(1), pages 157-168, January.

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