Abstract
This paper studies a single-period inventory problem with random yield and demand. In general, most of the previous works are based on the assumption of risk neutrality. We incorporate loss-averse preferences into this problem and the retailer’s objective is to maximize the expected utility. We obtain the retailer’s optimal ordering policy and then investigate the impact of loss aversion on it. Especially, if the shortage cost is small enough, the loss-averse retailer will always order less than the risk-neutral one. Moreover, the impacts of price and cost parameters on the loss-averse retailer’s optimal order quantity are analyzed. Then numerical experiments are conducted to illustrate our results.
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References
Grasman, S.E., Olsen, T.L., Birge, J.R.: Setting Basestock Levels in Multi-product Systems with Setups and Random Yield. IIE Trans. 40, 1158–1170 (2008)
Yano, C.A., Lee, H.L.: Lot Sizing with Random Yields: a Review. Oper. Res. 43, 311–334 (1995)
Wright, C.M., Mehrez, A.: An Overview of Representative Research of the Relationships between Quality and Inventory. Omega-Int. J. Manage. S. 26, 29–47 (1998)
Gerchak, Y., Vickson, R.G., Parlar, M.: Periodic Review Production Models with Variable Yield and Uncertain Demand. IIE Trans. 20, 144–150 (1988)
Wang, Y., Gerchak, Y.: Periodic Review Production Models with Variable Capacity, Random Yield, and Uncertain Demand. Manage. Sci. 42, 130–137 (1996)
Keren, B.: The Single-Period Inventory Problem: Extension to Random Yield from the Perspective of the Supply Chain. Omega-Int. J. Manage. S. 37, 801–810 (2009)
Inderfurth, K.: Analytical Solution for a Single-Period Production-Inventory Problem with Uniformly Distributed Yield and Demand. Cent. Eur. J. Oper. Res. 12, 117–127 (2004)
Fisher, M., Raman, A.: Reducing the Cost of Demand Uncertainty through Accurate Response to Early Sales. Oper. Res. 44, 87–99 (1996)
Kahn, J.A.: Why Is Production More Volatile than Sales? Theory and Evidence on the Stockout-Avoidance Motive for Inventory Holding. Q. J. Econ. 107, 481–510 (1992)
Kahneman, D., Tversky, A.: Prospect Theory: an Analysis of Decision under Risk. Econometrica 47, 263–291 (1979)
Schweitzer, M.E., Cachon, G.P.: Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence. Manage. Sci. 46, 404–420 (2000)
Wang, C.X., Webster, S.: The Loss-Averse Newsvendor Problem. Omega-Int. J. Manage. S. 37, 93–105 (2009)
Wang, C.X.: The Loss-Averse Newsvendor Game. Int. J. Prod. Econ. 124, 448–452 (2010)
Geng, W., Zhao, X., Gao, D.: A Single-Period Inventory System with a General S-shaped Utility and Exponential Demand. J. Syst. Sci. Syst. Eng. 19, 227–236 (2010)
Shen, H.C., Pang, Z., Cheng, T.C.E.: The Component Procurement Problem for the Loss-Averse Manufacturer with Spot Purchase. Int. J. Prod. Econ. 132, 146–153 (2011)
Wang, C.X., Webster, S.: Channel Coordination for a Supply Chain with a Risk-Neutral Manufacturer and a Loss-Averse Retailer. Decis. Sci. 38, 361–389 (2007)
Liu, W., Song, S., Wu, C.: Impact of Loss Aversion on the Newsvendor Game with Product Substitution. Int. J. Prod. Econ. (2012), doi:10.1016/j.ijpe.2012.08.017
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Liu, W., Song, S., Wu, C. (2013). The Loss-Averse Retailer’s Ordering Policy under Yield and Demand Uncertainty. In: Li, K., Li, S., Li, D., Niu, Q. (eds) Intelligent Computing for Sustainable Energy and Environment. ICSEE 2012. Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37105-9_23
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DOI: https://doi.org/10.1007/978-3-642-37105-9_23
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