Abstract
This study describes the stochastic problem of allocating finite capacity to booking requests with non-homogeneous Poisson processes with the consideration of cancellation, no-show, and overbooking and investigates the dynamic acceptance-or-rejection decision method for the revenue management of airline and hotel industries. In the airline problem, there is more than one fare class. For the hotel problem, multiple room classes, multiple price classes, and multiple stay lengths are considered. The decision maker would like to select the requests that customers are willing to pay a higher price by rejecting a current lower price customer, which may result in unused capacity. In addition, there is a possibility that an accepted request will be cancelled at a late time or even will not show up at the end time of booking horizon. If there are more requests than the available capacity at the departure time or check-in day, an overbooking penalty has to be paid for an unfulfilled customer. With the uncertain future demand arrivals, a correct acceptance-or-rejection decision for a current arrival request has to be made rapidly. This study proposes simulated expected revenue approach (SERA) that is modified from simulated expected revenue gap (SERG) originally proposed by Lai (2010). In the hotel problem, there are three different levels of problem complexities in this study. The first level considers single room class with single price class, and the second level involves single room class with multiple price classes. The third level considers multiple room classes with multiple price classes. In addition, there are two problems with two different assumptions in the third level due to allowing upgrading. In the first case, changing room during a customer’s stay is allowed. In the second case, changing room during a customer’s stay is prohibited. Based on the experiment results, SERA performs the best among all the tested approaches. Moreover, SERA is very robust under various problem conditions. Keywords: hotel management; airline management; non-homogeneous Poisson process; cancellation; no-show; overbooking; upgrade; decision support system.