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.
Highlights
The concept of city air terminal (CAT) is put forward to provide the air passenger services in urban area of a large city [1]
By accounting three aspects as optimization objectives, an efficient genetic algorithm is proposed for optimizing the locations of city air terminals
Three aspects including the average distance from passengers to city air terminals, the maximum tolerable distance, and the maximum terminal volume are considered in the optimization objective function
Summary
The concept of city air terminal (CAT) is put forward to provide the air passenger services in urban area of a large city [1]. The initial objective of CAT is to increase the convenience of the airport with offering airline check-in facilities in city center. Zhou et al.: An Efficient Genetic Method for Multi-objective Location Optimization of Multiple City Air Terminals. Zhang et al [12] applied a two-phase heuristics algorithm for solving the facility location problem. Pour and Nosraty [15] described a heuristic algorithm for solving the plant/facility location problem by applying ant-colony optimization meta-heuristic. To continue improving the service quality, it is important to optimize the location of CATs by considering multiple objectives, which include:.
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