Integration methods for aircraft scheduling and trajectory optimization at a busy terminal manoeuvring area

Abstract

This paper deals with the problem of efficiently scheduling take-off and landing operations at a busy terminal manoeuvring area (TMA). This problem is particularly challenging, since the TMAs are becoming saturated due to the continuous growth of traffic demand and the limited available infrastructure capacity. The mathematical formulation of the problem requires taking into account several features simultaneously: the trajectory of each aircraft should be accurately predicted in each TMA resource, the safety rules between consecutive aircraft need to be modelled with high precision, the aircraft timing and ordering decisions have to be taken in a short time by optimizing performance indicators of practical interest, including the minimization of aircraft delays, travel times and fuel consumption. This work presents alternative approaches to integrate various modelling features and to optimize various performance indicators. The approaches are based on the resolution of mixed-integer linear programs via dedicated solvers. Computational experiments are performed on real-world data from Milano Malpensa in case of multiple delayed aircraft. The results obtained for the proposed approaches show different trade-off solutions when prioritizing different indicators. KeywordsAir traffic • Take-off and landing operations • Optimal control • Job shop scheduling • Mixed-integer linear programming • Pareto efficiency B Marcella Samà

Optimising aircraft arrivals in terminal airspace by mixed integer linear programming model

“…Objectives minimise total average delay, minimising maximum delay and reducing delay differences among aircraft. Samà et al 23 propose a mixed integer linear programming approach for ASP. The model includes a trajectory optimisation process.…”

“…Constraint set (18) is added in the second step to limit the TCRT values that are found in the first step. Constraints (19)(20)(21)(22)(23) are sign constraints. Constraint set (24) limits the upper and lower bounds of the time travelled per unit distance between each waypoint due to airspeed reduction.…”

“…The first step uses the constraints (3-4), (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) and (19)(20)(21)(22)(23)(24) while the second step uses the constraints (3-4), (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24). The baseline scenario uses the objective (1) and constraints (3-5), (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) and (19)(20)(21)…”

Lost and Found: Predicting Airline Baggage At-risk of Being Mishandled

“…Also, there has been recent research related to airport operations management (Atkin et al, 2019). However, they focus on other areas of airport operations related to gate assignment (Dijk et al, 2019), aircraft landing and take-off coordination (Samà et al, 2019), and design of baggage storage systems (Yalcin et al, 2019). However, these papers do not speak to the problem we are addressing.…”

Airport operations management

“…To solve this problem, the authors apply a variant of recoverable robustness to find a solution that can be made feasible for a set of scenarios generated on the basis of historical data. Samà et al (2019) consider a combined trajectory management and sequencing problem for aircraft landing and taking-off from an airport, aiming to improve a variety of performance indicators, illustrating the trade-offs within this combined problem, and considering the benefits of three different approaches to combining the problems. Computational experiments are performed on real-world data from Milano Malpensa airport.…”