Application of Complex Network Theory and Genetic Algorithm in Airline Route Networks

Abstract

To cope with increasing customer demand and market changes, airline companies need to organize and manage their route networks in a more cost-efficient way. In addition, the robustness of flight operations against unpredictable accidents such as terrorist attacks and natural disasters has become more important to airlines. In this study, the concepts and techniques from complex network theory are used to model airline route networks, and then an effective and efficient genetic algorithm is developed to optimize airline route networks in terms of network properties that may have crucial roles to play in improving the cost-effectiveness and reliability of airspace systems. The simulation results illustrate that the work reported in this study has a good potential to improve the topology of airline route networks in terms of given network properties such as operating costs and network robustness.

Taxis in road pricing zone: should they pay the congestion charge?

“…Computation of demand matrices. Compute total demand q l ð Þ rs according to elastic demand function Equation (26) and use the logit-based mode choice model, that is, Equation (22)…”

“…In this paper, however, to solve the sensitivities from the VI program Equation (39) is very difficult in view of the complexity of the formulation. Compared with some other algorithms such as projection-based algorithm, descent algorithm, or penalty function approach, GA would still be a straightforward and effective method to solve the bi-level problem, which has been proven in many existing literatures (Liu et al [26]). Thus, in this paper, the GA is adopted for its simplicity and effectiveness.…”

Ripple-Spreading Network Model Optimization by Genetic Algorithm

“…As large-scale parallel stochastic search and optimization algorithms, genetic algorithms (GAs), if properly designed, have the capability of producing high quality solutions to NP-hard problems in an acceptable period of time [19]. Actually, GAs have already been used to optimize some network structures, for example, the topology optimization of CCS7 network [20], MPLS network [21] and airline route networks [22]. However, in such studies on network topology optimization, search is carried out by directly adding new links or removing existing links between nodes.…”

“…As is well known, permutation representation based GAs are often confronted with feasibility problems, which means random evolutionary operations, such as mutation and crossover, may generate some chromosomes whose associated solutions are invalid or infeasible against the underlying physical meaning of a real-world solution. For example, in the case of airline route network optimization, permutation representation based GA might establish a link between two very close cities, which is economically impracticable [22]. The RSNM makes it possible to discard permutation representation based GA, and to employ the very original binary representation based GA for network topology optimization.…”

Robustness of airline route networks

“…Other studies have analyzed the robustness of air transport network in order to determine which airports can be considered critical if they were to cease operations. That stream of research considers the regional (Lacasa et al, 2009;Liu et al, 2011) or global (Lordan et al, 2014b) levels of analysis. Finally, Cento (2009); Reggiani et al (2010) and Lordan (2014) have addressed the study of route networks of individual airlines, and Lordan et al (2015) has analyzed the robustness of airline alliance route networks.…”