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

Abstract

Some airports construct several buildings in city centre to offer check in and other services, denoted as city air terminals, which help passengers to check in and drop off luggage closer to their residences. Multi-objective location optimization methods play an important role in planning the locations of city air terminals. The objectives are to improve passenger experience and enhance the competitiveness of air transportation. A mathematical model of this problem is introduced. 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 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. Finally, a case study based on a large city in China is performed to test the proposed method for locating city air terminals in urban area and to show its effectiveness and efficiency.

Keywords

• Evolutionary computation
• genetic algorithm
• transportation
• ripple spreading algorithm