An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning
The objective of this study is to minimize the overall transportation cost through the joint decision-making for air-cargo hub network design and fleet planning under the uncertain environment. This joint decision-making considers various factors, including hub location, node connectivity, fleet siz...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/5754231 |
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| author | Yu Wang Tao Zhu Kaibo Yuan Peiwen Zhang Zhe Liang Jinfu Zhu |
| author_facet | Yu Wang Tao Zhu Kaibo Yuan Peiwen Zhang Zhe Liang Jinfu Zhu |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | The objective of this study is to minimize the overall transportation cost through the joint decision-making for air-cargo hub network design and fleet planning under the uncertain environment. This joint decision-making considers various factors, including hub location, node connectivity, fleet size, and flight frequency. It takes into account several uncertain parameters such as air-cargo demand and transportation cost in a realistic setting. We propose a mixed-integer programming model tailored to the characteristics of such problem, which utilizes interval numbers to address these challenges. This model aims to provide a robust scheme for the joint hub network design and the fleet planning in the uncertain environment. An improved probability-based interval ranking method is proposed to solve the model. This transformation converts the proposed model into an equivalent real-number one, simplifying the solving process. Then a hybrid heuristic algorithm, combining the advantages of Memory-Based Genetic Algorithm (MBGA) and Greedy Heuristic Procedure (GHP), is introduced to enhance the solving speed. Finally, the performance of our proposed model and algorithm is verified using real-world data from the Australian postal dataset. The results show that the proposed model reduces hub construction costs by 1.37% and fleet operational costs by 7.60%, respectively, as opposed to the use of traditional approaches. The computational time of the proposed algorithm is reduced by 28.4% and 36.5%, respectively, when compared to the use of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) algorithm. |
| format | Article |
| id | doaj-art-3ac930468a9848389e01a5ad37fa6732 |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-3ac930468a9848389e01a5ad37fa67322025-08-20T02:48:55ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/5754231An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet PlanningYu Wang0Tao Zhu1Kaibo Yuan2Peiwen Zhang3Zhe Liang4Jinfu Zhu5School of Economics and ManagementSchool of Economics and ManagementSchool of Economics and ManagementSchool of Economics and ManagementSchool of Economics & ManagementCollege of Civil AviationThe objective of this study is to minimize the overall transportation cost through the joint decision-making for air-cargo hub network design and fleet planning under the uncertain environment. This joint decision-making considers various factors, including hub location, node connectivity, fleet size, and flight frequency. It takes into account several uncertain parameters such as air-cargo demand and transportation cost in a realistic setting. We propose a mixed-integer programming model tailored to the characteristics of such problem, which utilizes interval numbers to address these challenges. This model aims to provide a robust scheme for the joint hub network design and the fleet planning in the uncertain environment. An improved probability-based interval ranking method is proposed to solve the model. This transformation converts the proposed model into an equivalent real-number one, simplifying the solving process. Then a hybrid heuristic algorithm, combining the advantages of Memory-Based Genetic Algorithm (MBGA) and Greedy Heuristic Procedure (GHP), is introduced to enhance the solving speed. Finally, the performance of our proposed model and algorithm is verified using real-world data from the Australian postal dataset. The results show that the proposed model reduces hub construction costs by 1.37% and fleet operational costs by 7.60%, respectively, as opposed to the use of traditional approaches. The computational time of the proposed algorithm is reduced by 28.4% and 36.5%, respectively, when compared to the use of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) algorithm.http://dx.doi.org/10.1155/2024/5754231 |
| spellingShingle | Yu Wang Tao Zhu Kaibo Yuan Peiwen Zhang Zhe Liang Jinfu Zhu An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning Journal of Advanced Transportation |
| title | An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning |
| title_full | An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning |
| title_fullStr | An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning |
| title_full_unstemmed | An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning |
| title_short | An Interval Integrated Optimization to Air-Cargo Hub Network Design and Airline Fleet Planning |
| title_sort | interval integrated optimization to air cargo hub network design and airline fleet planning |
| url | http://dx.doi.org/10.1155/2024/5754231 |
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