Optimal Booking Control for Physical Examination considering Late Cancellation and Overbooking

Background: Late cancellation of physical examination has a severe impact on the profit of a healthcare center since it is often too late to ll the vacancy. A booking control policy that considers overbooking is then one natural solution.Case presentation: In this study, we consider a healthcare center providing different examination sets using dierent resources. As each resource has its unique cost, revenue, and capacity, the optimal booking limits of all examination sets are hard to be calculated. We propose a probabilistic optimization model that maximizes the expected prot given the late cancellation probability of each type of customer, where the probabilities are estimated through logistic regression and customer grouping using historical booking and cancellation records. To test the performance of our proposed solution, we collaborate with a leading healthcare center. We simulate the presence and absence of customers generated by historical records and compare different strategies of overbooking.Conclusions: Through the experiment, we show that our method can significantly increase the expected profit of the healthcare center by around 11%.

Joint Decision Model of Group Ticket Booking Limits and Individual Passenger Dynamic Pricing for the High-Speed Railway

Rail operators in many countries discount group tickets to improve revenue by increasing price-driven demand. For individual passengers, dynamic pricing is beneficial for maximizing revenue based on the price discrimination principle. Usually, group fares are cheaper than individual fares. If too many group tickets are sold, there will not be enough tickets available to meet high-priced individual demand; by contrast, if not enough group tickets are sold and there is insufficient individual demand, the unsold seats will not have value once the train departs. Therefore, for railway operators, it is worth looking for a balance between group discounts and dynamic pricing to maximize benefits. Essentially, rail operators need to find the symmetry point of the expected revenue between accepting group bookings and reserving tickets for individuals when making decisions. In this study, we formulated a joint decision model of group ticket booking control and dynamic pricing and investigated the effect of the joint decision. The results of numerical experiments showed that incorporating group discounts into dynamic pricing can improve expected revenue when passenger demand is weak, and compared to setting fixed quantities for group tickets, dynamically controlling the limit of group bookings can effectively increase expected revenue. Further analysis of the impacts of time, number of tickets sold, and group demand was helpful to implement the proposed joint policy.