An Ant Colony Optimization Algorithm for the Multi-Objective Gate Assignment Problem

Abstract

In this research, the Gate Assignment Problem (GAP) is considered with reference to two objectives. The first objective is to minimize total walking distance of passengers to departure gates, check-in counters, and connection flights. The second one is to minimize number of flights assigned to the apron. Essentially, the GAP seeks the optimal flight-to-gate assignments so that total passenger walking distances and consequent connection times are minimized. A weighted sum of two objectives to the GAP is used by adding a penalty to flights assigned to the apron. An algorithm is used based on the Ant Colony Optimization (ACO) method to minimize our bi-objective gate assignment problem. Our goal is to apply the ACO to the GAP by achieving a near optimal solution. The behavior of each ant is critical for fast convergence to (near) optimal solutions of the GAP. The numerical experiments investigate whether the ACO approach is an efficient method to obtain good solutions in our proposed GAP model. The performance of this approach is evaluated in a variety of test problems. The commercial Gurobi Solver and a First Come First Served heuristic algorithm are used as baselines to compare our ACO results for small and large size instances, respectively. An instance generator was used to generate data for arrival and departure times, and additional specifications of flights.