In the Zone: Novel Approaches to Airplane Boarding

Despite the increased pressure on airlines to increase productivity in recent times, a largely overlooked inefficiency in air travel is the boarding and unloading process. The typical airline uses a zone system, where passengers board the plane from back to front in several groups. The efficiency of the zone system has come into question with the introduction of the open-seating policy of Southwest Airlines. Despite conventional wisdom, Southwest is able to turnaround planes at an uncanny rate with their innovative methods. Hence, the optimality of the entire boarding process has come into question. We propose a stochastic agent-based simulation of the boarding process in order to explore the effectiveness of novel boarding techniques. Our model organizes the aircraft into discrete units called ‘processors.’ Each processor corresponds to a physical row of the aircraft. Passengers enter the plane and are moved through the aircraft based on the functionality of these processors. During each cycle of our simulation each row (processor) can execute a single operation. Operations accomplish functions such as moving passengers to the next row, stowing luggage or seating passengers. The processor model tells us, from an initial ordering of passengers in a queue, how long the plane will take to board, and produces a grid detailing the chronology of passenger seating. We extend our processor model with a genetic algorithm, which we use to search the space of possible passenger configurations for innovative and effective patterns. This algorithm employs the biological techniques of mutation and crossover to seek out locally optimal solutions to the passenger boarding problem. We create a variant of this algorithm which is designed to optimize a priori boarding patterns. We also integrate a Markov chain model of passenger preference with our processor model. We use this preference model to produce a simulation of Southwest-style boarding, where seats are not assigned but are chosen by individuals based on environmental constraints (such as seat availability).