Overbooking Model Research Articles

A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits

This paper develops a stochastic mathematical overbooking model (SMOM) for determining the optimal number of patient appointments to accept to maximize expected total profits for diverse healthcare environments. Overbooking is necessary to alleviate the detrimental effects of no-shows that are experienced by healthcare providers. Compared with traditional simple deterministic overbooking approaches, SMOM is unique since it considers the probability distributions of no-shows and walk-ins to obtain the optimal solution. Usually, healthcare providers would only have no-show data based on their current practices, so the authors provide a method to extrapolate the conditional probability to estimate what happens when overbooking occurs. SMOM is then compared with two alternative strategies: the base case of no overbooking and the naive statistical overbooking approach (NSOA) that simply adds the mean number of no-shows minus the mean number of walk-ins to the number of appointments to accept. It is shown using data collected for 59 physicians in a medical clinic that SMOM compared with the base case can increase profits by 43.72% on average whereas NSOA improves profits by 29.66% on average. Sensitivity analyses demonstrate SMOM is robust under a diversity of healthcare environments and cost structures.

Hotel Room-inventory Management: An Overbooking Model

Hotel Room-inventory Management: An Overbooking Model

Hotel room-inventory management: an overbooking model

The gentle art of overbooking can be augmented with a carefully constructed computer model— but human expertise will always be necessary.

Airline overbooking in the multi-class case

This paper presents an airline overbooking model at a class level for one service compartment–cabin. Class level demand data is used to determine the number of bookings that can be taken for each class. The model is optimised through the use of multi-dimensional search routines. The control level model developed is tested with data supplied by Ireland's national airline, Aer Lingus. The model shows a significant improvement over previous methods employed by Aer Lingus and was subsequently adopted by the airline.

Airline overbooking in the multi-class case

This paper presents an airline overbooking model at a class level for one service compartment–cabin. Class level demand data is used to determine the number of bookings that can be taken for each class. The model is optimised through the use of multi-dimensional search routines. The control level model developed is tested with data supplied by Ireland's national airline, Aer Lingus. The model shows a significant improvement over previous methods employed by Aer Lingus and was subsequently adopted by the airline.

Booking Control Increases Profit at Scandinavian Airlines

Planes are overbooked to avoid empty seats. Yet, there is a trade-off of increased upgradings, downgradings, and denied boardings. The patterns of reservations, cancellations, and no-shows are random. Thus, determining the best overbooking levels involves stochastic multistage optimal decision making and time series analysis. An overbooking model for a nonstop flight with two types of passengers is based on a statistical description of the booking process and two-dimensional stochastic dynamic programming. It produces, apparently for the first time, an optimal policy for a real case. The model has been validated and used at Scandinavian Airlines. The net potential revenue increase was about two million US dollars per year.

Booking policy for flights with two types of passengers

An overbooking model for a fixed nonstop flight with two types of passengers is presented. The model takes cancellations, and reservations prior to departure into consideration as well as ‘no-shows’ (passengers who fail to arrive for flights without notice), denied boardings and downgrading of passengers. The model treats the airline booking process as a Markovian nonhomogeneous sequential decision process. The model is solved by two dimensional stochastic dynamic programming. Computational experiences and numerical results from a real-life case, including different types of sensivity analysis, are presented.

An Inventory Depletion Overbooking Model For the Hotel Industry

Travelers who have no intention of showing up often fail to cancel their hotel reservations in a timely manner. Because of this tendency, hotels in turn protect their revenue-generating but highly perishable inventory of rooms by overbooking in anticipation of no-shows and late cancellations. Unfortunately, the literature and a recent survey of hotels indicate that hotel overbooking levels are largely set by trial and error and may lead either to oversales and displaced guests or to an unprofitably high number of empty rooms and lost revenues. This study examines the institutional parameters and operating constraints of the hotel industry and treats overbooking as an inventory problem of fixed supply and variable demand, and proposes a statistically derived inventory depletion overbooking model that judiciously balances the opportunity cost of empty rooms with the adverse consequences of oversales. The model thus allows hotel managers to systematically establish optimal booking levels.

An Airline Overbooking Model. Marvin Rothstein. Transportation Science, Vol. 5, No. 2, May 1971, pp. 180-192. Transportation Science Section, Operations Research Society of America, 428 East Preston Street, Baltimore, Maryland 21202. $3

An Airline Overbooking Model. Marvin Rothstein. Transportation Science, Vol. 5, No. 2, May 1971, pp. 180-192. Transportation Science Section, Operations Research Society of America, 428 East Preston Street, Baltimore, Maryland 21202. $3