Solving Airport Gate Assignment Problem Using an Improved Genetic Algorithm with Dynamic Topology

2018 ◽  
pp. 877-884
Author(s):  
Ran Xu ◽  
Kaiquan Cai
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Improved Genetic Algorithm ◽  
Dynamic Topology ◽  
Gate Assignment

A New Genetic Algorithm for Continuous Berth and Quay Crane Allocation Problem

2011 ◽  
Vol 97-98 ◽  
pp. 619-622 ◽  
Author(s):  
Na Li ◽  
Zhi Hong Jin ◽  
Erick Massami
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Container Terminal ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Improved Genetic Algorithm ◽  
Berth Allocation Problem ◽  
The Real ◽  
Quay Cranes ◽  
Quay Crane Assignment

The combined optimization of continuous berth allocation problem and quay crane assignment problem are solved. Considering the real constraints of container terminal, an improved genetic algorithm is proposed. The chromosome is composed of berthing time, berthing location and number of quay cranes. While in the following, specific quay cranes are fixed to assign to ships. Through comparisons with the former two literatures, the results are improved averagely by 33.78% and 28.57% respectively by the proposed genetic algorithm, which shows its effectiveness.

scholarly journals An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bingjie Liang ◽  
Yongliang Li ◽  
Jun Bi ◽  
Cong Ding ◽  
Xiaomei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Large Scale ◽  
Computational Time ◽  
Time Interval ◽  
Parallel Genetic Algorithm ◽  
Elite Strategy ◽  
Calculation Results ◽  
Gate Assignment ◽  
The Stability

Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.

An improved genetic algorithm with dynamic topology

2016 ◽  
Vol 25 (12) ◽  
pp. 128904 ◽  
Author(s):  
Kai-Quan Cai ◽  
Yan-Wu Tang ◽  
Xue-Jun Zhang ◽  
Xiang-Min Guan
Keyword(s):  
Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Dynamic Topology
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