We study the revenue management (RM) problem encountered in airport car parks, with the primary objective to maximize revenues under a continuous-time framework. The implementation of pre-booking systems for airport car parks has spread rapidly around the world and pre-booking is now available in most major airports. Currently, most RM practises in car parks are simple adjustments of those developed for hotels, exploiting the similarities between the two industries. However, airport car parks have a distinct setting where the price per day of a parking space is heavily discounted by the length of stay (LoS) of the booking. This is because the customer decision tends to be made after the length of the trip is already set, and it becomes a choice between parking or alternative modes of transport. Consequently, the LoS becomes a critical variable for revenue optimization. Since customers are able to book the parking by the minute, the resulting state space is very large, making a conventional network solution intractable . Instead, decomposed single-resource problems need to be considered. Here we develop a bid-price control strategy to manage the bookings and propose novel approaches to define such bid prices depending on the LoS, which could be utilized in real-time RM algorithms. Managing stochastic car park bookings by LoS in the decomposed single-resource approximation allowed us to achieve within 5% of the expected revenues for a multi-resource approximation, with a fraction of the computational effort. When expected demand exceeds the available parking capacity, the method increases the revenues by up to 45% relative to the first come, first served acceptance policy.
- revenue management
- Dynamic programming