An Evolutionary Optimization Approach to Maximize Runway Throughput Capacity for Hub and Spoke Airports

Abstract

The airports have emerged as a major bottleneck in the air transportation network. Thus during the busiest time, optimal utilization of the limited airport resources such as runways and taxiways can help to avoid the congestion and delay as well as increase the airport capacity. This problem is further aggravated by use of Hub-Spoke model by airlines which sees a burst of medium size aircraft arrival followed by few heavy aircraft departure. To address this problem, strategic as well as efficient tactical approaches are essential to deal with arrivals and departures. In this paper, we propose an evolutionary optimization approach to maximize the runway throughput capacity for integrated arrival and departure in a single runway scenario. An evolutionary computation based Genetic Algorithm (GA) is developed to optimize and integrate a stream of arriving and departing aircraft sequence for a given time window. The evolved optimal arrival and departure sequencing was analyzed using the Time-Space diagrams for different aircraft configuration.The distribution shows that in Hub airports heavy and large aircrafts are sequenced consecutively where in Spoke airports similar aircraft (i.e., medium (M)-medium (M), large (L)-large (L) and so on) are positioned side by side to reduce the process time. Simulation result also shows that proposed model obtained optimal sequence that takes lower processing time as well as achieves a higher throughput comparing to First Come First Serve (FCFS) approach commonly used for arriving and departing aircraft.