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Value-At-Risk Optimal Policies for Revenue Management Problems

Research output: Working paper

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Value-At-Risk Optimal Policies for Revenue Management Problems. / Koenig, M; Meissner, J.

Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Research output: Working paper

Harvard

Koenig, M & Meissner, J 2010 'Value-At-Risk Optimal Policies for Revenue Management Problems' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Koenig, M., & Meissner, J. (2010). Value-At-Risk Optimal Policies for Revenue Management Problems. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Koenig M, Meissner J. Value-At-Risk Optimal Policies for Revenue Management Problems. Lancaster University: The Department of Management Science. 2010. (Management Science Working Paper Series).

Author

Koenig, M ; Meissner, J. / Value-At-Risk Optimal Policies for Revenue Management Problems. Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Bibtex

@techreport{f111b96169394a269ce0214294b06a24,
title = "Value-At-Risk Optimal Policies for Revenue Management Problems",
abstract = "Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios. We state the underlying problem as a Markov decision process and provide a computational method for computing policies, which optimise the value-at-risk for a given confidence level. This is achieved by computing dynamic programming solutions for a set of target revenue values and combining the solutions in order to attain the requested multi-stage risk-averse policy. Numerical examples and comparison with other risk-sensitive approaches are discussed.",
keywords = "capacity control, revenue management, risk, value-at-risk",
author = "M Koenig and J Meissner",
year = "2010",
language = "English",
series = "Management Science Working Paper Series",
publisher = "The Department of Management Science",
type = "WorkingPaper",
institution = "The Department of Management Science",

}

RIS

TY - UNPB

T1 - Value-At-Risk Optimal Policies for Revenue Management Problems

AU - Koenig, M

AU - Meissner, J

PY - 2010

Y1 - 2010

N2 - Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios. We state the underlying problem as a Markov decision process and provide a computational method for computing policies, which optimise the value-at-risk for a given confidence level. This is achieved by computing dynamic programming solutions for a set of target revenue values and combining the solutions in order to attain the requested multi-stage risk-averse policy. Numerical examples and comparison with other risk-sensitive approaches are discussed.

AB - Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios. We state the underlying problem as a Markov decision process and provide a computational method for computing policies, which optimise the value-at-risk for a given confidence level. This is achieved by computing dynamic programming solutions for a set of target revenue values and combining the solutions in order to attain the requested multi-stage risk-averse policy. Numerical examples and comparison with other risk-sensitive approaches are discussed.

KW - capacity control

KW - revenue management

KW - risk

KW - value-at-risk

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Value-At-Risk Optimal Policies for Revenue Management Problems

PB - The Department of Management Science

CY - Lancaster University

ER -