Event‐based allocation of airline check‐in counters: a simple dynamic optimization method supported by empirical data

Abstract This paper studies the real‐life problem of dynamically optimizing the number of airport check‐in counters to allocate for a single flight. The main feature of our work is the use of empirical data collected at the Singapore Changi Airport, which drives the dynamic optimization model of a parallel queues system. We propose an event‐based dynamic programming model that simplifies considerably the optimization analysis even for large‐scale problems with 700+ booked passengers. We investigate the following research questions: (a) For a particular flight, what is the optimal number of counters the system should open with and what is the corresponding optimal total cost? (b) Given the state of the system at any event epoch, should we open another counter or not and what is the optimal cost‐to‐go from this state? The empirical data we collected at the airport are used to test the assumptions, estimate the key parameters, and run the computational experiments. We apply our model to 14 flights at the Singapore Changi Airport and identify cases in which, depending on the cost parameters, the model advocates the use of either a dynamic or a static policy. Although the model concerns only an exclusive‐use system, it is flexible enough to apply to other configurations such as a common‐use system or a single‐queue, multicounter system.