Testing New Methods for Boarding a Partially Occupied Airplane Using Apron Buses

Abstract

The use of apron buses has become a common practice at many European airports. Previous studies related to airplane boarding rarely apply when apron buses are used, leaving airlines with no well-researched option except to use the random boarding method. In this paper, we test the time to complete boarding a two-door airplane using various boarding methods with two apron buses. These methods were inspired by the classical outside-in, back-to-front, and reverse-pyramid methods considering the limited number of boarding groups corresponding to the limited number of apron buses used for transporting passengers from the terminal to the aircraft. Unlike earlier publications, we test these methods under partial aircraft occupancy. Furthermore, we test the boarding methods under conditions involving: different passenger occupancy rates, different luggage situations, and with two types of seating assignments-random and based upon passenger seating preferences-by considering the advantages brought by the symmetric layout of the aircraft. Experimental results indicate that the best performing method can reduce the boarding time by up to 38.6% compared to the time resulting from the random boarding method. When the airplane is partially occupied, the best performing methods are reverse pyramid-A, hybrid-A, and hybrid-B, all with similar performances.

Greedy Method for Boarding a Partially Occupied Airplane Using Apron Buses

“…In these figures, passengers assigned to the first apron bus (i.e., first group) are designated in yellow seats while those passengers assigned to the second apron bus (i.e., second group) are designated in blue seats. Considering that Ferrari and Nagel [27] assert that the efficiency of a boarding strategy may depend on the airplane occupancy, in [16], the 13 methods have been tested on partially occupied flights, with an occupancy ranging between 60% and 90%. Two cases for the seat assignments have been considered for the simulations: random seat assignment and random with preferences for seat assignments.…”

“…The passenger movement assumptions are in line with [2,16] as we test the proposed greedy method against the three best-performing methods of the literature for this problem in [16], Reverse Pyramid-A, Hybrid-A and Hybrid-B, by considering the same initial conditions.…”

“…The storing time depends on the number of hand luggage carried by a passenger as well as the already-stored luggage. Similar to Audenaert et al [55] and [2,3,6,11,16], we allow unlimited overhead bin capacity and employ the following formula to determine luggage storage time:…”

“…The values corresponding to the observed boarding time data from those other researchers have been transformed into ticks, the time unit used by NetLogo, by dividing it by 1.2 and rounding it up to the nearest integer [2]. Similar to [16], we introduce several carry-on luggage combinations (see Table 1): The S7 situation considers that none of the passengers will bring any carry-on bags on board, therefore, in this case, we expect to get zero aisle interferences.…”

“…We provide the rules associated with the greedy method and programmed the model in NetLogo [15] by taking advantages of the agent-based modeling properties. Through the use of the BehaviorSpace tool [15], the model is simulated and compared in terms of average boarding times with the best-performing methods in the literature, namely Reverse Pyramid-A, Hybrid-A, and Hybrid-B [16]. An Airbus A320 configuration is used, having 30 rows, a single economy class and two boarding doors located in the front and in the rear side of the airplane as presented in Figure 2.…”