July  2012, 8(3): 577-590. doi: 10.3934/jimo.2012.8.577

Single-period inventory model with discrete stochastic demand based on prospect theory

1. 

Department of Automation, Tsinghua University, Beijing 100084, China, China, China

Received  March 2011 Revised  November 2011 Published  June 2012

This paper studies a single-period inventory (newsvendor) problem with discrete stochastic demand. In general, most of the previous works are based on the expected profit/cost criterion or expected utility criterion. We consider the effect of irrational factor under uncertainty and therefore incorporate prospect theory into inventory model. Our objective is to maximize the overall value of the prospect, which can be calculated by using the value function and the weighting function. For any given initial inventory level, it can be shown that a state-dependent order-up-to policy is optimal. Further, the optimal policy has a simple structure, and the retailer can easily decide whether to place an order or not. Moreover, the impacts of parameters on the optimal policy are illustrated through numerical experiments.
Citation: Wei Liu, Shiji Song, Cheng Wu. Single-period inventory model with discrete stochastic demand based on prospect theory. Journal of Industrial & Management Optimization, 2012, 8 (3) : 577-590. doi: 10.3934/jimo.2012.8.577
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show all references

References:
[1]

Manufacturing & Service Operations Management, 2 (2000), 410-423. Google Scholar

[2]

Econometrica, 21 (1953), 503-546. doi: 10.2307/1907921.  Google Scholar

[3]

Journal of Finance, 56 (2001), 1247-1292. doi: 10.1111/0022-1082.00367.  Google Scholar

[4]

Journal of Industrial and Management Optimization, 2 (2006), 297-318.  Google Scholar

[5]

Management Science, 41 (1995), 786-794. doi: 10.1287/mnsc.41.5.786.  Google Scholar

[6]

Operations Research, 44 (1996), 87-99. doi: 10.1287/opre.44.1.87.  Google Scholar

[7]

Journal of Systems Science and Systems Engineering, 19 (2010), 227-236. doi: 10.1007/s11518-010-5128-8.  Google Scholar

[8]

Quarterly Journal of Economics, 107 (1992), 481-510. doi: 10.2307/2118479.  Google Scholar

[9]

Econometrica, 47 (1979), 263-291. doi: 10.2307/1914185.  Google Scholar

[10]

European Journal of Operational Research, 174 (2006), 1643-1650. doi: 10.1016/j.ejor.2005.03.047.  Google Scholar

[11]

Omega-International Journal of Management Science, 27 (1999), 537-553. doi: 10.1016/S0305-0483(99)00017-1.  Google Scholar

[12]

European Journal of Operational Research, 94 (1996), 29-42. doi: 10.1016/0377-2217(95)00192-1.  Google Scholar

[13]

Operations Research, 45 (1997), 54-65. doi: 10.1287/opre.45.1.54.  Google Scholar

[14]

Chinese Control and Decision Conference, (2009), 2539-2544. Google Scholar

[15]

Operations Research, 47 (1999), 183-194. doi: 10.1287/opre.47.2.183.  Google Scholar

[16]

Management Science, 46 (2000), 404-420. doi: 10.1287/mnsc.46.3.404.12070.  Google Scholar

[17]

International Journal of Production Economics, 129 (2011), 65-78. doi: 10.1016/j.ijpe.2010.09.002.  Google Scholar

[18]

Journal of Risk and Uncertainty, 5 (1992), 297-323. Google Scholar

[19]

International Journal of Production Economics, 124 (2010), 448-452. doi: 10.1016/j.ijpe.2009.12.009.  Google Scholar

[20]

Omega-International Journal of Management Science, 37 (2009), 93-105. doi: 10.1016/j.omega.2006.10.003.  Google Scholar

[21]

European Journal of Operational Research, 196 (2009), 544-553. doi: 10.1016/j.ejor.2008.04.002.  Google Scholar

[22]

Working paper, 2009. Available from: http://ioe.engin.umich.edu/techrprt/pdf/TR09-04.pdf. Google Scholar

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