Airport taxi planning: Lagrangian decomposition

Abstract

SUMMARYThe airport taxi planning (TP) module is a decision tool intended to guide airport surface management operations. TP is defined by a flow network optimization model that represents flight ground movements and improves aircraft taxiing routes and schedules during periods of aircraft congestion. TP is not intended to operate as a stand-alone tool for airport operations management: on the contrary, it must be used in conjunction with existing departing and arriving traffic tools and overseen by the taxi planner of the airport, also known as the aircraft ground controller. TP must be flexible in order to accommodate changing inputs while maintaining consistent routes and schedules already delivered from past executions. Within this dynamic environment, the execution time of TP may not exceed a few minutes. Classic methods for solving binary multi-commodity flow networks with side constraints are not efficient enough; therefore, a Lagrangian decomposition methodology has been adapted to solve it. We demonstrate TP Lagrangian decomposition using actual data from the Madrid-Barajas Airport.

An online speed profile generation approach for efficient airport ground movement

“…However, the underlying difficulty with these approaches often makes the computational cost too high for real-world problems. This is especially true when airport traffic is subject to unexpected events and a fast re-planning is mandatory (Clare and Richards, 2011;Marí n, 2013). In view of this, methods based on sequential planning have been proposed (Lesire, 2010;Ravizza et al, 2014;Zhang et al, 2016).…”

Conflict‐free time‐based trajectory planning for aircraft taxi automation with refined taxiway modeling

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execution of taxi plans suffers from unexpected events like mechanical failures and human errors. As a result, the planning process must run in real time in practice and be integrated with other system modules to update planning results when new data become available [8].

State of the artThe taxi trajectory planning problem is handled using mixed integer linear programming (MILP) formulations in [9][10][11][12].

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“…An inherent problem of the MILP approach is the high computational demand. To make the solution method efficient enough to be integrated with other airport management modules, the Lagrangian decomposition method is used in [8]. Clare and Richards [12] achieved the scalability of the presented MILP formulation by means of the receding horizon scheme and the iterative conflict avoidance strategy.Genetic algorithm (GA) based optimization is another commonly used approach in airport taxi trajectory planning [13][14][15][16][17][18][19][20].…”

“…According to the recent researches, an average solution time of more than 1 min is required to find optimal taxi trajectories for a few aircraft even after introducing the receding horizon and iterative conflict avoidance strategy to improve the computational performance. And this solution time will increase remarkably with the number of aircraft within the planning horizon [8,12]. To realize realtime optimization of taxi routes and RTAs, Lesire [24] utilizes a fast sequential planning method, which determines taxi routes and RTAs for one aircraft after another.…”

“…execution of taxi plans suffers from unexpected events like mechanical failures and human errors. As a result, the planning process must run in real time in practice and be integrated with other system modules to update planning results when new data become available [8].…”