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900. Price optimization for car sharing
Invited abstract in session MB-59: Revenue Management in Sharing/Platform Economy, stream Pricing and Revenue Management.
Monday, 10:30-12:00Room: S08 (building: 101)
Authors (first author is the speaker)
| 1. | Rym M'Hallah
|
| Engineering, King's College London | |
| 2. | Christine Currie
|
| School of Mathematics, University of Southampton | |
| 3. | Beatriz Brito Oliveira
|
| INESC TEC, Faculty of Engineering, University of Porto |
Abstract
Car sharing could support the transition toward net zero by reducing private car usage. Its success depends on its financial sustainability to service providers and attractiveness to end users. Dynamic pricing could incentivize users, balance supply and demand, and improve the cost-effectiveness and attractiveness of car sharing.
We describe a fast method for optimizing the hourly rental price charged to a car sharing customer where the price may depend on the number of cars already on hire. The usage of the fleet can be described by a continuous time Markov chain model, which can be reduced to a multi-server queueing model under relatively unrestrictive assumptions. The analytical tractability of the queueing model enables fast optimization to maximize expected hourly revenue for either a single fare system or a system in which the fare depends on the number of cars on hire, while accounting for stochasticity in customer arrival times and the durations of hire. This allows for the development of dynamic pricing strategies for car sharing and supports answering more strategic questions such as the optimal fleet size.
We present the optimal prices for a given customer population and arrival rate and show how the expected revenue and car availability depend on the arrival rate into the system, the willingness-to-pay distribution, and the size of the customer population. We present the results of experiments showing the optimal fleet size for a given customer population.
Keywords
- Revenue Management and Pricing
- Optimization Modeling
- Transportation
Status: accepted
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