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A multi‐emission‐driven efficient network design for green hub‐and‐spoke airline networks

Authors :
Mengyuan Sun
Yong Tian
Xingchen Dong
Yangyang Lv
Naizhong Zhang
Zhixiong Li
Jiangchen Li
Source :
IET Intelligent Transport Systems, Vol 18, Iss 2, Pp 346-376 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract The green hub‐and‐spoke airline network (GHSAN) is emerging as a dominant feature due to its excellent economic and environmental‐friendly capabilities. However, environmental GHSAN designs still have some concerns, including single pollutant‐domain oversimplification and lack of comprehensive network‐level operation impacts. This paper proposes a multi‐emission‐driven efficient network design approach for GHSAN, utilizing a system, green, and user threefold optimization methodology. The approach includes a multi‐objective optimization model and a two‐layer solving method. The multi‐objective optimization aims at minimizing multiple emissions, including carbon dioxide, carbonic oxide hydrocarbon, and nitric oxide, while also considering transportation system costs and journey user costs. A two‐layer optimization algorithm is adopted to address different scales of optimization. Real‐world results demonstrate that the proposed method mitigates environmental impact and user costs and increases overall airline density in airline networks. The proposed method can have a 16.29% reduction in green‐fold (10 nodes) and a 12.06% decrease in user costs for the user‐fold (10 nodes). As the number of nodes (15, 25, 50 nodes) and hubs (3, 4, 5, 6, 7 hubs) increase, the genetic algorithm (GA) proves to be more efficient and suitable in large‐scale GHSAN. This work is further significant for the long‐term and sustainable development of the future air transport industry.

Details

Language :
English
ISSN :
17519578 and 1751956X
Volume :
18
Issue :
2
Database :
Directory of Open Access Journals
Journal :
IET Intelligent Transport Systems
Publication Type :
Academic Journal
Accession number :
edsdoj.fe80c338f6c74ce486d4342f827fa8a0
Document Type :
article
Full Text :
https://doi.org/10.1049/itr2.12455