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

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

Blog Article

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 mel axolotl is proposed to solve the model.This transformation converts the proposed model into an equivalent real-number one, simplifying the solving process.Then Jeans 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.