Maximizing Revenues of Perishable Assets with a Risk Factor

Feng, Youyi; Xiao, Baichun

doi:10.1287/opre.47.2.337

Cited by 63 publications

(28 citation statements)

References 4 publications

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“…This result is consistent with other studies of dynamic pricing with no demand learning where the optimal price is found to be non-increasing with the remaining inventory (Chatwin, 2000;Feng and Xiao, 1999;Zhao and Zheng, 2000). In addition, we can see in Fig.…”

Section: Observationsupporting

confidence: 92%

Dynamic Pricing With Updated Demand for the Sports and Entertainment Ticket Industry

Phumchusri¹,

Swann²

2014

Journal of Computer Science

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Revenue Management (RM) helped increase profitability for many travel industries. Selling perishable products with a fixed event date, the Sports and Entertainment (S&E) ticket industry can potentially benefit from RM ideas but has received less attention in the literature. In this study we develop dynamic pricing models for stochastic S&E demand in a discrete finite time setting, where demand depends not only on ticket prices but also on remaining times until the show dates. We assume the show popularity is uncertain to the seller, but this information can be learned via Bayesian updates as early sales are revealed. We present stochastic dynamic programs for Sports and Entertainment tickets pricing decisions. We test the models using real data obtained from a major performance venue in the U.S. to understand properties of the model solutions and performance under different scenarios. Our results show that demand learning is most beneficial when the initial estimates are incorrect. In addition, we found it is less necessary for the seller to vary price every period if demand variation is low and/or a large amount of demand arrives close to the show dates. Overall, we found that the benefits from having flexibility of price changes and demand learning can complement each other to achieve as much as 8.15% revenue increase on average, as compared to static pricing.

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Section: Observationsupporting

confidence: 92%

Dynamic Pricing With Updated Demand for the Sports and Entertainment Ticket Industry

Phumchusri¹,

Swann²

2014

Journal of Computer Science

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“…The first revenue management model incorporating risk, the model of Feng and Xiao [10], considers a single-resource problem with two given prices and allows only one price change. They define risk by sales variance as a result of price changes.…”

Section: Related Workmentioning

confidence: 99%

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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“…One distinguishing feature of the work presented here is the emphasis on the convergence in distribution of the normalized revenue. Most other work has focused solely on expected revenues (one exception being Feng and Xiao 1999, where variability effects are factored into the decision model). In addition, we allow for arrival processes other than the compound Poisson process or its discrete analog.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Behavior of an Allocation Policy for Revenue Management

Cooper

2002

Operations Research

140

124

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Revenue management has become an important tool in the airline, hotel, and rental car industries. We describe asymptotic properties of revenue management policies derived from the solution of a deterministic optimization problem. Our primary results state that, within a stochastic and dynamic framework, solutions arising out of a single well-known linear program can be used to generate allocation policies for which the normalized revenue converges in distribution to a constant upper bound on the optimal value. We also show similar asymptotic results for expected revenues. In addition, we describe counterintuitive behavior that can occur when allocations are updated during the booking process (updating allocations can lead to lower expected revenue). These results add to the understanding of allocation policies and help to make concrete the statement that simple policies from easy-to-solve formulations can be relatively effective, even when analyzed in the more realistic stochastic and dynamic framework.

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Maximizing Revenues of Perishable Assets with a Risk Factor

Cited by 63 publications

References 4 publications

Dynamic Pricing With Updated Demand for the Sports and Entertainment Ticket Industry

Dynamic Pricing With Updated Demand for the Sports and Entertainment Ticket Industry

Risk management policies for dynamic capacity control

Asymptotic Behavior of an Allocation Policy for Revenue Management

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