An Efficient Genetic Method for Multi-Objective Location Optimization of Multiple City Air Terminals

Abstract

Multi-objective location optimization methods play an important role in intelligent planning the locations of city air terminals. The objectives are to improve passenger experience and enhance the competitiveness of air transportation. A mathematical model for the problem is introduced at first. The model takes three factors into accounted as the optimization objectives, including the average path length from passengers to city air terminals, the maximum tolerable distance of passengers, and the service capacity limit of a station. Secondly, an efficient hybrid evolutionary method is presented for efficiently optimizing the locations of city air terminals, which includes an improved ripple-spreading algorithm for solving the many-to-many path optimization problem and a genetic algorithm for optimizing the facility location problem. A case study based on a large city in China is performed to test the proposed method for configuring city air terminals in city urban area and to show its effectiveness and efficiency.INDEX TERMS Multi-objective optimization, hybrid evolutionary method, ripple spreading algorithm, adaptive genetic algorithm, city air terminal, optimization method.Recently, researchers have shown an increased interest in