Formulation of the airport collaborative gate allocation problem and the Bee Colony Optimization solution approach

Abstract

This article formulates the Collaborative Gate Allocation (CGA) problem as a novel concept to reduce aircraft gate-waiting time and to increase aircraft gate utilization. The proposed CGA concept assumes voluntary collaboration and negotiation among airlines and airport operators, enabling them to share real-time information on gate-utilization and gate-sharing. The proposed policy is especially applicable to U.S. airports, as an alternative to exclusive or preferential gate-sharing practices, at terminal buildings that accommodate a large number of smaller airlines during periods of high congestion and delays caused by high traffic demands, bad weather conditions, or any ad hoc situations. The CGA problem belongs to the class of complex combinatorial optimization problems, whose optimal solution is difficult to discover. This article develops a Swarm Intelligence-based model for the CGA problem. Our approach to solving the CGA problem is based on the Bee Colony Optimization (BCO) metaheuristic. The BCO algorithm belongs to the class of population-based algorithms. This technique uses a similarity between the way in which bees in nature look for nectar, and the way in which optimization algorithms search for an optimum solution to a combinatorial optimization problem. Numerical experiments are performed using Denver International Airport as a real-world case study. We show that the proposed CGA problem can be efficiently solved by our BCO algorithm, and that applications of the CGA concept can significantly reduce gate delays.