Robust Management of Airport Security Queues Considering Passenger Non-compliance with Chance-Constrained Optimization

TL;DR

This paper introduces a chance-constrained optimization model for airport security queue management that accounts for passenger non-compliance, significantly reducing waiting times and improving efficiency.

Contribution

It presents a novel chance-constrained approach to optimize passenger arrival recommendations, explicitly considering non-compliance uncertainty.

Findings

85% reduction in total waiting time at Barcelona airport

30% improvement over deterministic models assuming full compliance

Effective management of arrival times reduces congestion and delays

Abstract

The long waiting time at airport security has become an emergent issue as demand for air travel continues to grow. Not only does queuing at security cause passengers to miss their flights, but also reduce the amount of time passengers spend at the airport post-security, potentially leading to less revenue for the airport operator. One of the key issues to address to reduce waiting time is the management of arrival priority. As passengers on later flights can arrive before passengers on earlier flights, the security system does not always process passengers in the order of the degree of urgency. In this paper, we propose a chance-constrained optimization model that decides in which time slot passengers should be recommended to arrive. We use chance constraints to obtain solutions that take the uncertainty in passenger non-compliance into account. The experimental results, based on a sample day of flight schedules at the Barcelona airport, show a reduction of 85% in the total waiting time. Compared to the deterministic case, in which passengers are assumed to fully comply with the recommendations, we see a 30% increase in the reduction of the total waiting time. This highlights the importance of considering variation in passenger compliance in the management of airport security queues.