The use of meta-heuristics for airport gate assignment

Improper assignment of gates may result in flight delays, inefficient use of the resource, customer’s dissatisfaction. A typical metropolitan airport handles hundreds of flights a day. Solving the gate assignment problem (GAP) to optimality is often impractical. Meta-heuristics have recently been proposed to generate good solutions within a reasonable timeframe. In this work, we attempt to assess the performance of three meta-heuristics, namely, genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and a hybrid approach based on SA and TS. Flight data from Incheon International Airport are collected to carry out the computational comparison. Although the literature has documented these algorithms, this work may be a first attempt to evaluate their performance using a set of realistic flight data.

► This work discusses different formulations for the gate assignment problem.
► We examine the use of meta-heuristics and their hybridization.
► We conduct a computational study using realistic flight data.