Effective Management of Airport Security Queues with Passenger Reassignment

TL;DR

The paper tackles long airport security queues by reassigning passengers to predefined security-crossing time slots using a Minimum Cost Network Flow (MCNF) formulation, enabling exact polynomial-time solutions via network simplex. It defines a delta-minute slot framework, a cost function that penalizes early and late arrivals relative to each passenger's flight time, and a capacity-aware queuing model to balance throughput and wait times. Through a numeric study using BCN data, the authors demonstrate substantial reductions in total waiting time and overall cost under both time-invariant and time-varying capacity scenarios, with capacity smoothing enhancing staffing practicality. They also analyze robustness to compliance uncertainty, showing that the approach tolerates some deviations in passenger behavior while still delivering meaningful improvements, and they outline future work in distributed robust optimization to further bolster resilience.

Abstract

Airport security queues often suffer from inefficiencies that result in long wait times and decreased throughput, especially at peak departure time, affecting both passengers and airlines. This work addresses the problem of reassigning passengers to specific time slots for crossing security, aiming to mitigate these inefficiencies. We frame this problem as a Minimum Cost Network Flow (MCNF) problem, enabling us to solve it exactly in polynomial time due to its linear programming structure. Our approach redistributes passenger demand across different time intervals. By optimizing the reassignment of passengers to sigma-minute time slots, we achieve significant improvements in throughput and reductions in waiting time. Preliminary results demonstrate the effectiveness of our method in enhancing operational efficiency and passenger satisfaction. The MCNF formulation offers a scalable and adaptable solution, providing long-term benefits for airport security management.

Figures (9)

• Figure 1: Flow network representation of passengers and time slots with a sink node. Arrows denote feasible flows from passengers to time slots and from time slots to the sink node.
• Figure 2: The cost of reassigning passengers to different time slots with respect to the passenger's flight departure time.
• Figure 3: N-T diagram of the system under first-come first-served control (left) and under the optimal control policy (right) derived from the optimization where the capacity is not allowed to vary with time.
• Figure 4: Variation in the deviation of the arrival-times of passengers for 3 different values of the constant capacities. Note that lesser values for the capacity constrains the system and promotes inequality, whereas the optimal solution is more equitable for all passengers for a higher value of the capacity.
• Figure 5: Increase in the capacity yields a monotonous decrease in the objective value, mainly because of the structure of the cost function.