An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem
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 g...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8880390 |
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| _version_ | 1832566471379648512 |
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| author | Bingjie Liang Yongliang Li Jun Bi Cong Ding Xiaomei Zhao |
| author_facet | Bingjie Liang Yongliang Li Jun Bi Cong Ding Xiaomei Zhao |
| author_sort | Bingjie Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-440562f7aa4e4226aa84125eb70fd9b3 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-440562f7aa4e4226aa84125eb70fd9b32025-02-03T01:04:03ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88803908880390An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment ProblemBingjie Liang0Yongliang Li1Jun Bi2Cong Ding3Xiaomei Zhao4School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaInformation Science & Technology Department, Beijing Capital International Airport Co., Ltd., Beijing 100621, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaGate 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.http://dx.doi.org/10.1155/2020/8880390 |
| spellingShingle | Bingjie Liang Yongliang Li Jun Bi Cong Ding Xiaomei Zhao An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem Journal of Advanced Transportation |
| title | An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem |
| title_full | An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem |
| title_fullStr | An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem |
| title_full_unstemmed | An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem |
| title_short | An Improved Adaptive Parallel Genetic Algorithm for the Airport Gate Assignment Problem |
| title_sort | improved adaptive parallel genetic algorithm for the airport gate assignment problem |
| url | http://dx.doi.org/10.1155/2020/8880390 |
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