Multiple-Allocation Hub-and-Spoke Network Design With Maximizing Airline Profit Utility in Air Transportation Network

Airlines commonly need to take into consideration maximizing their profit while designing the hub-and-spoke network to obtain more market share and promote healthy development of aviation industry. Hence, in this article, we study the problem of multiple-allocation HUb and spoke network design for R...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 25; H. 7; S. 7294 - 7310
Hauptverfasser: Yu, Nan, Dong, Bin, Qu, Yuben, Zhang, Mingwei, Chen, Guihai, Tan, Qingqing, Wang, Yanyan, Dai, Haipeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Air transportation
Airline industry
Airline profit
Airlines
Airports
Algorithms
Approximation
approximation algorithm
Approximation algorithms
Aviation
cardinality
Costs
customer preference
Flight
greedy algorithm
hub location problem
Industrial development
Maximization
Network design
Optimization
Resource management
Routing
routing allocation mode
submodular set function
Transportation
Transportation networks
transportation profit utility
ISSN:1524-9050, 1558-0016
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Zusammenfassung:Airlines commonly need to take into consideration maximizing their profit while designing the hub-and-spoke network to obtain more market share and promote healthy development of aviation industry. Hence, in this article, we study the problem of multiple-allocation HUb and spoke network design for ROuting flight flows to maximize airline profit utility (HURO). That is, given a set of airport nodes, a set of flight flows with known origin positions and destination positions, finding a limited number of hub edges to transfer flows and determining routing allocation mode considering customer preference such that the overall transportation profit utility is maximized. To address HURO problem, we first consider a relaxed version of HURO (HURO-R for short). We prove that HURO-R falls into the realm of maximizing a submodular set function subject to a cardinality constraint, and propose an algorithm with a constant approximation ratio. Next, we design a two-level algorithm framework with a constant approximation ratio to address HURO. Besides, we consider variants of HURO, HURO-C and HURO-RU, and design approximation algorithms to address them. We conduct simulation experiments on standard dataset and field experiments to verify our theoretical findings. The results shows that our proposed algorithm can outperform other comparison algorithms by 75.28 percent.
Bibliographie:ObjectType-Article-1
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3348466