Modelling a bi-objective airport gate scheduling with controllable processing time using hybrid NSGA-II and VNS algorithm

Abstract

In this research, we address a bi-objective model in a more realistic situation such that airport gate processing time is controllable. It is assumed that the possible compression/expansion processing time of a flight can be continuously controlled. The aim is simultaneously: 1) minimise the total cost of tardiness, earliness, delay and compression as well as expansion costs of job processing time; 2) minimise the passengers overcrowding on gate. In this study, a mixed-integer programming model is proposed. For solving the problem, two multi-objective meta-heuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and hybrid NSGA-II and variable neighbourhood search (VNS) are applied. VNS is used for preventing the solution from trapping in the local optimum, instead of mutation operator in NSGA-II. The algorithms are tested with the real life data from Mehrabad International Airport. Computational experiments reveal that hybrid NSGA-II and VNS generate better Pareto-optimal solution as compared to NSGA-II.

Optimizing airport slot scheduling problem using optimization algorithms

“…Planes are diverted to an apron if they are unable to be allocated to a gateway because of overlaps. In addition, in a more realistic setting where airport gate processing time is controllable, Khatibi et al ( 2019 ) suggested a bi-objective framework to lower the overall impact of task processing time expansion, unavailability, earliness, latency, and compression. The project also seeks to reduce the number of passengers crowded onto gates.…”