Revenue management in the car rental industry: A stochastic programming approach

Haensel, Alwin; Mederer, Michael; Schmidt, Henning

doi:10.1057/rpm.2010.52

Cited by 33 publications

(31 citation statements)

References 11 publications

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“…Specifically he proposes a PNLP heuristic and develops a static booking limit policy. Similarly, Haensel et al (2012) address a capacity allocation and shuttling decision problem for a car rental network within a fixed pick-up period. A two stage stochastic programming (SP) is proposed to the development of booking limit policies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Typical airline RM models (e.g., Talluri and van Ryzin, 2004) usually assume that the capacity allocation decisions are made upon the arrival of each customer, which is however unrealistic and costly in car rental. Due to this difference, the airline RM literature typically considers the bid-price heuristics while in car rental booking limits are widely implemented (e.g., Schmidt, 2009, Haensel et al, 2012. To construct the dynamic booking limit policies a complex non-linear programming problem must be solved, whereas the construction of bid price policies is straightforward after the decomposition (see, e.g., Talluri and van Ryzin, 2004).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In reality, a car rental station may have both inbound and outbound shuttling. To explicitly model the outbound shuttling, we need to model the demands for the outbound shuttling (see, e.g., Haensel et al, 2012), which may significantly complicate the model and the…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Dynamic booking control for car rental revenue management: A decomposition approach

Pang

2017

European Journal of Operational Research

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Highlights• We consider dynamic booking controls for car rental revenue management.• A decomposition approach and two dynamic heuristic policies are proposed.• Their strong revenue performance is in contrast to the PNLP policy used in practice.• The proposed policies are robust to vehicle transhipment cost while PNLP not.• The PNLP policy might still work well in peak seasons and downtown rental stations. Different from conventional airline revenue management, car rental revenue management needs to take into account not only the existing bookings but also the lengths of the existing rentals and the capacity flexibility via fleet shuttling, which yields a high-dimensional system state space. In this paper, we formulate the dynamic booking control problem as a discrete-time stochastic dynamic program over an infinite horizon.Such a model is computationally intractable. We propose a decomposition approach and develop two heuristics. The first heuristic is an approximate dynamic program (ADP) which approximates the value function using the value functions of the decomposed problems. The second heuristic is constructed directly from the optimal booking limits computed from the decomposed problems, which is more scalable compared to the ADP heuristic. Our numerical study suggests that the performances of both heuristics are close to optimum and significantly outperform the commonly used probabilistic non-linear programming (PNLP) heuristic in most of the instances. The dominant performance of our second heuristic is evidenced in a case study using sample data from a major car rental company in the UK.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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Dynamic booking control for car rental revenue management: A decomposition approach

Pang

2017

European Journal of Operational Research

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“…airline seats) is divided into differentiated subset inventories, and the sale or withholding from sale of these subset inventories is carefully managed across time and sales channels to meet demand for the purpose of maximising realised total revenue and profits. RM techniques have long been employed as standard practice within the airline (Cross, 1995;Kimes and Wirtz, 2015), hotel (Forgacs, 2010;Hayes and Miller, 2011;Kimes, 1989Kimes, , 2003Tranter, Stuart-Hill and Parker, 2011) and rental car industries (Carroll and Grimes, 1995;Cretin and Scuto, 2013;Haensel, Mederer and Schmidt, 2012) and have subsequently been adopted by a number of other industries.…”

Section: Introduction To Rmmentioning

confidence: 99%

Demand-based pricing in the US theme park industry

Lewison

2017

IJLTM

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Even as revenue management practices spread from the travel sector to other industries, the US theme park industry practiced static pricing. Highly visible demand-based pricing introduced by Disney and Universal highlights the relative lack of research on theme park pricing. We surveyed the research and gathered pricing information from 70 different US theme park websites in 2012 and 2016 and found that US theme parks in our sample added online ticket sales, discounts for advance sales and differential daily pricing during that period. We also report the proportion of US theme parks offering queue products and meal plans as ticket add-ons. Characteristics of theme parks may prevent true dynamic pricing, but the US theme park industry has begun moving away from its traditional static pricing model towards demand-based pricing.

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“…They focus on examining different methods for generating the scenarios trees, and numerical results are provided for small and medium sized networks. Recently, Haensel, Mederer, and Schmidt (2011) have proposed a stochastic mixed integer programming model for the rental car industry.…”

Section: Introductionmentioning

confidence: 99%

Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

Escudero

Monge

Morales

et al. 2013

Transportation Science

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T his paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. We solve the model by means of the so-called expected future value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive approach. We compare our revenues to those obtained by additive bid prices, such as the bid prices derived from the deterministic equivalent model (DEM) of the compact representation of the SP model. Our computational experience shows that the revenues obtained by our approach are better for middlerange values of the load factor of demand, whereas the differences among all the approaches we have tested are insignificant for extreme values. Moreover, our approach requires significantly less computation time than does the optimization of DEM by plain use of optimization engines. Problem instances with 72 pairs of bundle-fare classes have been solved in less than one minute, with 800 pairs in less than five minutes, and with 4,000 pairs in less than one hour. The time taken by DEM was, in general, of one order of magnitude higher. Finally, for the three largest problem instances, and after two hours, the expected revenue returned by DEM was below that obtained by EFV by 13 47% 17 14%, and 38 94%, respectively.

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Revenue management in the car rental industry: A stochastic programming approach

Cited by 33 publications

References 11 publications

Dynamic booking control for car rental revenue management: A decomposition approach

Dynamic booking control for car rental revenue management: A decomposition approach

Demand-based pricing in the US theme park industry

Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

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