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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Bibliographic Details
Published in:Journal of advanced transportation 2024-11, Vol.2024 (1)
Main Authors: Wang, Yu, Zhu, Tao, Yuan, Kaibo, Zhang, Peiwen, Liang, Zhe, Zhu, Jinfu
Format: Article
Language:English
Subjects:
Air freight
Aircraft
Airlines
Algorithms
Analysis
Aviation
Cargo
Computing time
Construction costs
Cost control
Decision making
Design
Design factors
Design optimization
Efficiency
Genetic algorithms
Genetic transformation
Greedy algorithms
Heuristic methods
Integer programming
Mixed integer
Network design
Operating costs
Optimization
Parameter uncertainty
Problem solving
Rankings
Citations: Items that this one cites
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Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2024/5754231