An Interval Integrated Optimization to Air‐Cargo Hub Network Design and Airline Fleet Planning

• 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
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2024/5754231

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.