A model for airline seat control considering revenue uncertainty and risk

Huang, Kuancheng; Chang, Ko Chen

doi:10.1057/rpm.2009.19

Cited by 18 publications

(28 citation statements)

References 19 publications

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“…Huang and Chang [7] modify the decision function for the dynamic capacity control model in order to make it risk-sensitive in terms of mean versus standard deviation. The modified decision function relaxes the riskneutral optimal decision using a discounted marginal seat value.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, recent approaches by Barz and Waldmann [6] and Huang and Chang [7] propose risk-averse policies for the dynamic capacity control model. This model is introduced a standard revenue management model by Lee and Hersh [5] and is originally stated as a dynamic programming formulation of a risk-neutral policy.…”

Section: Introductionmentioning

confidence: 99%

“…Barz and Waldmann [6] analyse the dynamic capacity control model under constant absolute risk aversion using an exponential utility as the objective function in the dynamic programming recursion. Huang and Chang [7] present a policy which includes a discount factor not in the objective but in the decision function. This discount factor actually determines a risk premium for certainty of earning revenue now, instead of under uncertainty later.…”

Section: Introductionmentioning

confidence: 99%

“…This kind of risk premium is more easily communicated to practitioners than the exponential utility function, where the computation of the risk premium requires certain knowledge about the distribution of the demand function. Huang and Chang [7] also propose a policy considering the selling history and conduct an extensive analysis for risk aversion, comparing standard deviations and Sharpe ratios of risk-neutral and risk-averse policies.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, we perform an analysis of the policies by numerical experiments and look at risk measures in terms of volatility by the standard deviation and in terms of downside risk by the conditional-valueof-risk (CVaR). We extend the analysis of Huang and Chang [7] and propose improved policies which are also easily implemented in practice. Furthermore, we introduce a new straightforward policy which provides acceptable results for moderate levels of risk aversion.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Risk management policies for dynamic capacity control

Koenig

Meissner

2015

Computers & Operations Research

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Consider a dynamic decision making model under risk with a fixed planning horizon, namely the dynamic capacity control model. The model describes a firm, operating in a monopolistic setting and selling a range of products consuming a single resource. Demand for each product is time-dependent and modeled by a random variable. The firm controls the revenue stream by allowing or denying customer requests for product classes. We investigate risk-sensitive policies in this setting, for which risk concerns are important for many non-repetitive events and short-time considerations.Numerically analyzing several risk-averse capacity control policies in terms of standard deviation and conditional-value-at-risk, our results show that only a slight modification of the risk-neutral solution is needed to apply a risk-averse policy. In particular, risk-averse policies which decision rules are functions depending only on the marginal values of the riskneutral policy perform well. From a practical perspective, the advantage is that a decision maker does not need to compute any risk-averse dynamic program. Risk sensitivity can be easily achieved by implementing risk-averse functional decision rules based on a risk-neutral solution.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Risk management policies for dynamic capacity control

Koenig

Meissner

2015

Computers & Operations Research

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Optimizing conditional value-at-risk in dynamic pricing

2018

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Optimizing the conditional value-at-risk in revenue management

Gönsch

Hassler

2013

Rev Manag Sci

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Many service industries use revenue management to balance demand and capacity. The assumption of risk-neutrality lies at the heart of the classical approaches, which aim at maximizing expected revenue. In this paper, we give a comprehensive overview of the existing approaches, most of which were only recently developed, and discuss the need to take risk-averse decision makers into account. We then present a heuristic that maximizes Conditional Value-at-Risk (CVaR). Although CVaR has become increasingly popular in finance and actuarial science due to its beneficial properties, this risk measure has not yet been considered in the context of revenue management. We are able to efficiently solve the optimization problem inherent in CVaR by taking advantage of specific structural properties that allow us to reformulate this optimization problem as a continuous knapsack problem. In order to demonstrate the applicability and robustness of our approach, we conduct a simulation study that shows that the new approach can significantly improve the risk profile in various scenarios.

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A model for airline seat control considering revenue uncertainty and risk

Cited by 18 publications

References 19 publications

Risk management policies for dynamic capacity control

Risk management policies for dynamic capacity control

Optimizing conditional value-at-risk in dynamic pricing

Optimizing the conditional value-at-risk in revenue management

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