Fleet and revenue management in car rental companies: A literature review and an integrated conceptual framework

Oliveira, Beatriz Brito; Carravilla, Maria Antónia; Oliveira, Jonice

doi:10.1016/j.omega.2016.08.011

Cited by 59 publications

(34 citation statements)

References 57 publications

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“…Car rental RM has some additional characteristics that make it quite challenging: extensive use of upgrades, one-way rentals, flexible and uncertain inventory (fleet management across stations, uncertain return stations and rental durations) and contractual versus walk-in customers. An overview is also given in Lieberman (2007) and a recent survey by Oliveira et al (2017). Guillen et al (2019) report on the components and success of EUROPCAR's RM systems, which has improved both the fleet utilization by around 3% and the revenue-per-day by around 2% in the markets France, Portugal, and Spain.…”

Section: Car Rentalmentioning

confidence: 99%

A review of revenue management: Recent generalizations and advances in industry applications

Klein

Koch

Steinhardt

et al. 2020

European Journal of Operational Research

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Section: Car Rentalmentioning

confidence: 99%

A review of revenue management: Recent generalizations and advances in industry applications

Klein

Koch

Steinhardt

et al. 2020

European Journal of Operational Research

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“…As an important strategy of revenue management, overbooking has been applied to various industries, including aviation [7], hotels [25], car rentals [26], and restaurants [27]. Soerag et al [28] propose an effective revenue management model that incorporate customer choice behaviors and cancellations.…”

Section: ) Overbooking In Generalmentioning

confidence: 99%

Optimal Overbooking Policy for Cloud Service Providers: Profit and Service Quality

2019

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A cloud federation is a current paradigm that enables partnered cloud providers to share idle capacities during low demand periods and to purchase spare resources during demand spikes. In this research, we propose an optimal overbooking policy to maximize federation members' profits and enhance cloud users' experiences. The proposed policy overcomes cloud providers' low utilization and increases their profits. Under the market-oriented cloud federation system, we use the number of idle resources in the cloud federation and the operational costs of those resources to help the provider decide on its instance exchange price. Under such a price mechanism, we develop an optimal overbooking model and identify the conditions necessary for optimal solutions. Through implementing the optimal mechanism, we observe that the proposed overbooking policy can improve a federated provider's profits and decrease the probability of service level agreement (SLA) violation. When a provider's capacity is relatively large and the provider adopts the proposed overbooking policy, it could achieve maximum profits and decline its SLA violation when it has unmet customer demands and there are idle resources in the cloud federation. Through establishing the cooperative game model of the cloud federation, we make a reasonable profit distribution based on Shapley value. The cloud provider's profits and the probability of the SLA violation change as the instance price, the distribution of unserved customers, the number of federation members and penalty cost change. Compared with the other overbooking policies and no overbooking mechanisms, our research improves profits and reduces cloud provider's overbooking risks, thereby presenting a win-win situation for both the individual providers and the cloud federation. INDEX TERMS Cloud federation, overbooking policies, optimization profit distribution, revenue management.

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“…Supply and demand are dynamically balanced, but there is still an overestimation or underestimation. Since demand determines the size of the leasing station inventory and fleet, demand estimation and forecasting techniques are worth studying [23]. The first reports of RM application to car rental were at Hertz and National Car Rental [24,25].…”

Section: Introductionmentioning

confidence: 99%

Unconstrained Estimation of Multitype Car Rental Demand

et al. 2021

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The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

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Fleet and revenue management in car rental companies: A literature review and an integrated conceptual framework

Cited by 59 publications

References 57 publications

A review of revenue management: Recent generalizations and advances in industry applications

A review of revenue management: Recent generalizations and advances in industry applications

Optimal Overbooking Policy for Cloud Service Providers: Profit and Service Quality

Unconstrained Estimation of Multitype Car Rental Demand

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