A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

Abstract

Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

Optimization of Integrated Departures and Arrivals Under Uncertainty

“…In past years, to improve the operational efficiency in terminal airspace, some researchers attempted to solve arrival scheduling problems. [1][2][3][4][5][6] Still others [7][8][9] addressed airport surface management problems, which are correlated to the efficiency of terminal airspace operations. When different arrival and/or departure flows share the same resources such as runways, waypoints and/or route segments, inefficient operations emerge because of the constraints of shared resources.…”

Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

“…The spatial planning focuses primarily on taxi routing between the gates and the runways (Balakrishnan and Jung, 2007;Keith et al, 2008;Gerdes and Temme, 2012;Guépet et al, 2016). The temporal planning focuses on scheduling of taxi activities, which is used to assign time stamps to aircraft concerning when to reach certain point on the airport surface along its taxi route (Smeltink and Soomer, 2004;Rathinam et al, 2008;Montoya et al, 2010). Regarding the objective of these optimization problems, many of the previous studies focus on minimizing the total taxi time between the runway and the gate (Pesic, et al, 2001;Deau et al, 2009;Ravizza et al, 2013), while others consider multi-objective optimization.…”

Implementation of an Optimization and Simulation-Based Approach for Detecting and Resolving Conflicts at Airports

“…Concerning problems related to the airspace, we can find many works about the sequencing and merging or scheduling of arrivals in the TMA (Beasley et al, 2000;Beasley et al, 2001;Hu and Chen, 2005;Michelin et al, 2009;Balakrishnan and Chandran, 2010;Zuniga et al 2011). On the other hand we can also find studies related to ground issues such as gate assignment problem (Bolat, 2000;Dorndorf et al, 2007;Kim and Feron, 2012;Narciso and Piera, 2015), scheduling of departures (Pujet et al, 1999;Rathinam et al 2009;Sandberg et al, 2014;Simaiakis and Balakrishnan, 2015) or airport surface management (Montoya et al, 2010;. In order to conduct a more precise analysis and obtaining an integrated view of the system, it is better to consider airspace and airside together, so that we can also consider the interactions between the two environments.…”