Robust Optimization for the Loss-Averse Newsvendor Problem

Yu, Hui; Zhai, Jia; Chen, Guang-Ya

doi:10.1007/s10957-016-0870-9

Cited by 17 publications

(18 citation statements)

References 24 publications

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“…Xu et al [18] investigated the newsvendor's option purchase decision when the excess demands could be replenished. When only the mean and variance of the demand distribution were known, Yu et al [19] considered the robust newsvendor problem and obtained the robust optimal ordering policy. Moreover, some researchers consider the loss-averse newsvendor's risk management and introduce CVaRmeasure into this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When λ = 1 and π 0 = 0, our model reduces to the risk-neutral one. Note that although multiple forms of loss aversion utility function are presented (e.g., [15,24,35,36]), among them this piecewise-linear one is most widely used in the economics, finance (e.g., [37,38]) and operations management literature (e.g., [15][16][17][18][19][20][21]23,24,26]) because of its simplicity.…”

Section: Model Descriptionmentioning

confidence: 99%

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The Loss-Averse Newsvendor Problem with Random Yield and Reference Dependence

et al. 2020

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This paper studies a loss-averse newsvendor problem with reference dependence, where both demand and yield rate are stochastic. We obtain the loss-averse newsvendor’s optimal ordering policy and analyze the effects of loss aversion, reference dependence, random demand and yield on it. It is shown that the loss-averse newsvendor’s optimal order quantity and expected utility decreases in loss aversion level and reference point. Then, that this order quantity may be larger than the risk-neutral one’s if the reference point is less than a negative threshold. In addition, although the effect of random yield leads to an increase in the order quantity, the loss-averse newsvendor may order more than, equal to or less than the classical one, which significantly depends on loss aversion level and reference point. Numerical experiments were conducted to demonstrate our theoretical results.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

The Loss-Averse Newsvendor Problem with Random Yield and Reference Dependence

et al. 2020

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“…To study the newsvendor's risk attitude, many researchers have adopted the utility approach such as (Meng et al 2017;Hui et al 2016;Wang and Webster 2009;Guo and Chen 2000;Eeckhoudt et al 1995). For instance, Meng et al (2017) employed anchoring as an approach for competitive newsvendors.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk premiums and certainty equivalents of loss-averse newsvendors of bounded utility

Dalalah

2019

J Ind Eng Int

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Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to model risk attitudes fails to explain some decision-making paradoxes. In contrast, this paper deals with the utility maximization of the newsvendor using a class of bounded utility functions to study the effect of loss aversion on the newsvendor certainty equivalents and risk premiums. New formulas are introduced to find the utility-optimal order quantity of the normal distribution. The results show that when an exponential loss aversion exists, the classical newsvendor optimal quantity serves as a lower bound when the overage costs are high and as an upper bound when the underage costs are high. In addition, we show that high loss aversion entails higher risk premiums. Similar conclusion holds when the overage/underage costs increase. Higher standard deviations, on the other hand, mean lower utility-optimal quantities and higher risk premiums. The presented formulas are advantageous in finding the optimal order quantities and risk premiums of a stochastic short-shelf life inventory when the loss is a key factor in the decision-making process.

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“…Gallego and Moon [5] provided a simpler proof of Scarf 's formula and extended his ideas. In the last 25 years, since the appearance of Gallego and Moon [5], there have been numerous results in the literature related to extensions of Scarf 's ordering rule to different settings and applications; see, for example, maximizing the worst-case expected profit as a target [6][7][8][9][10][11][12][13][14][15][16][17][18], minimax regret as a target [10,[19][20][21], and 2 Mathematical Problems in Engineering optimizing the worst-case conditional value-at-risk as a target [22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization for the Newsvendor Problem with Discrete Demand

Zhai

Sun

2018

Mathematical Problems in Engineering

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A robust newsvendor model with discrete demand is initiatively studied, and the steps to obtain the optimal ordering decision are provided. The study shows that the optimal ordering decision with discrete demand is very different from that with continuous demand. Besides, the total number of demand points has almost no effect on the performance of both ordering decisions. Furthermore, for an ultralow-profit product, the ordering decision with discrete demand performs significantly better than that with continuous demand.

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Robust Optimization for the Loss-Averse Newsvendor Problem

Cited by 17 publications

References 24 publications

The Loss-Averse Newsvendor Problem with Random Yield and Reference Dependence

The Loss-Averse Newsvendor Problem with Random Yield and Reference Dependence

Risk premiums and certainty equivalents of loss-averse newsvendors of bounded utility

Robust Optimization for the Newsvendor Problem with Discrete Demand

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