Comparison of Airport check-in allocation policies using agent-based simulation

Abstract: The airline industry has been one of the fastest growing industries in the past decades. This growth causes an increased strain on all parts of the industry, such as airports. In airports, one of the leading causes of passenger waiting is the check-in process. Through effective allocation of personnel to check-in counters it is possible to improve passenger experience whilst reducing delays and operating costs, without expanding the physical capacity of a check-in area. The purpose of this study is to investigate the performance of two counter allocation policies. The policies to be examined are from a previous study by Parlar et al. where they numerically compare the performance of a static and dynamic allocation method. For the dynamic method, the number of allocated counters can be changed at predetermined times, while for the static the same number of counters are open throughout the process. The methods were compared using two time scales, with 10, 30, 100 and 10, 30, 50 passengers respectively. For this study, an agent-based simulation was developed upon an implementation of RVO2, a simulation tool to replicate human movement and navigation. The simulations compared the total cost, consisting of passenger waiting time, counter operations and number of delayed passengers, of the different policies and scenarios. The results confirm the findings of previous work, that the dynamic policy is more cost effective when only considering the factors above. The results also indicate that disturbances in the process could lead to vastly increased costs as the allocation methods lack an overhead for deviations from expected operations. From these findings, we speculate that a variation on the previously proposed policies could bring about a positive impact on the airline industry, if implemented.