مقاله سال ۲۰۱۶

Due to the rapid civil aviation growth, the competition pressure increases among airlines. Therefore, airlines need to continually optimize their operations with the goal of maximizing their effi- ciency and profitability. One of the most promising ways is to reduce the airplane turn time, i.e., the time to unload an airplane after its arrival and to prepare it for departure again. A significant saving could be achieved by reducing the enplaning and deplaning time, since they are the main contributions to an airplane’s turn time. A successfully designed strategy-pairs for enplaning and deplaning is expected to perform satisfactorily to meet the needs of the three principal users: the airlines, airport operators and the passengers.

Airlines make every effort to minimize the time that their flights stay on the ground. Nyquist and McFadden (2008) pointed out that for each minute an active airplane stays on the ground, the airline needs to spend US $30. Thus, each minute saved in the turn time of a flight can accumulate to produce considerable annual savings. Reduction of airplane turn time can also benefit the airport operators in three aspects: firstly, it could reduce the flight delays caused by imbalances between demand and capacity by scheduling more flights (Ball et al., 2010). Secondly, it improves the passengers’ experience at airport terminals and consequently increases level of service of the airport; thirdly, it makes a more efficient utilization of the equipment on ground. For passengers, they are concerned about their own waiting time, and individual enplaning and deplaning time. Passengers generally prefer shorter enplaning and deplaning time. A reduction in total enplaning and deplaning time implies a reduction of the average individual enplaning and deplaning time for passengers.

Efforts have been made to reduce the enplaning time, and most of them are based on simulation works. Marelli et al. (1998) reported a discrete event simulation model and evaluated different enplaning scenarios and airplane interior configurations. Van Landeghem and Beuselinck (2002) discussed various enplaning strategies via computer simulation to study to what extent enplaning time can be reduced. Results have shown that the choiceof enplaning strategies highly influences the enplaning time, both totally and individually. Ferrari and Nagel (2005) evaluated robustness of strategies with three disturbances: early or late enplaning of passengers, dimensions of airplane, and the occupancy level of the airplane. Steffen (2008, 2012) presented the most time-saving strategy by applying a Markov Chain Monte Carlo optimization algorithm. Tang et al. (2012) explored the dynamic properties of passengers’ motions in enplaning process with consideration of passengers’ individual properties. Milne and Kelly (2014) and Qiang et al. (2014) emphasized the importance of luggage storage space and passengers were assigned to seats based on the number of luggage they carried.