Variable Neighborhood Search Algorithm for the Single Assignment Incomplete Hub Location Problem with Modular Capacities and Direct Connections

AL Athamneh, Raed; Tanash, Moayad; Bani Hani, Dania; Rawshdeh, Mustafa; Alawin, Abdallah; Albataineh, Zaid

doi:10.1016/j.orp.2023.100286

Operations Research Perspectives

2023

DOI: 10.1016/j.orp.2023.100286

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Variable Neighborhood Search Algorithm for the Single Assignment Incomplete Hub Location Problem with Modular Capacities and Direct Connections

Raed AL Athamneh,

Moayad Tanash,

Dania Bani Hani

et al.

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2024

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Cited by 1 publication

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

Wang,

Zhu,

Yuan

et al. 2024

Journal of Advanced Transportation

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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.

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

Wang,

Zhu,

Yuan

et al. 2024

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

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Variable Neighborhood Search Algorithm for the Single Assignment Incomplete Hub Location Problem with Modular Capacities and Direct Connections

Cited by 1 publication

References 48 publications

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

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