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Multiobjective Gate Assignment Model Considering Carbon Emissions

It has been a main concern for governments to reduce the carbon emission of the aviation industry. The paper proposes a multiobjective gate assignment model that considers the carbon emission at the airport surface to facilitate environmental-friendly airport construction. Three objectives are consi...

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Detalles Bibliográficos
Autores principales: Shan, Yixuan, Shao, Yuwei, Yuan, Qi, Jiang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001770/
https://www.ncbi.nlm.nih.gov/pubmed/36900962
http://dx.doi.org/10.3390/ijerph20053952
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author Shan, Yixuan
Shao, Yuwei
Yuan, Qi
Jiang, Yu
author_facet Shan, Yixuan
Shao, Yuwei
Yuan, Qi
Jiang, Yu
author_sort Shan, Yixuan
collection PubMed
description It has been a main concern for governments to reduce the carbon emission of the aviation industry. The paper proposes a multiobjective gate assignment model that considers the carbon emission at the airport surface to facilitate environmental-friendly airport construction. Three objectives are considered in the model to reduce carbon emissions, including the proportion of flights assigned to the contact gate, aircraft taxiing fuel consumption, and gate assignment robustness. In order to achieve better performance on all objectives, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to obtain the optimal results. The operation data from a domestic airport is deployed to validate the model. The optimal results of the gate assignment model are compared with the original scheme. It indicates that the proposed model can effectively reduce carbon emissions. The study can provide a strategy for gate assignment to reduce carbon emissions and improve the management of the airport.
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spelling pubmed-100017702023-03-11 Multiobjective Gate Assignment Model Considering Carbon Emissions Shan, Yixuan Shao, Yuwei Yuan, Qi Jiang, Yu Int J Environ Res Public Health Article It has been a main concern for governments to reduce the carbon emission of the aviation industry. The paper proposes a multiobjective gate assignment model that considers the carbon emission at the airport surface to facilitate environmental-friendly airport construction. Three objectives are considered in the model to reduce carbon emissions, including the proportion of flights assigned to the contact gate, aircraft taxiing fuel consumption, and gate assignment robustness. In order to achieve better performance on all objectives, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to obtain the optimal results. The operation data from a domestic airport is deployed to validate the model. The optimal results of the gate assignment model are compared with the original scheme. It indicates that the proposed model can effectively reduce carbon emissions. The study can provide a strategy for gate assignment to reduce carbon emissions and improve the management of the airport. MDPI 2023-02-23 /pmc/articles/PMC10001770/ /pubmed/36900962 http://dx.doi.org/10.3390/ijerph20053952 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shan, Yixuan
Shao, Yuwei
Yuan, Qi
Jiang, Yu
Multiobjective Gate Assignment Model Considering Carbon Emissions
title Multiobjective Gate Assignment Model Considering Carbon Emissions
title_full Multiobjective Gate Assignment Model Considering Carbon Emissions
title_fullStr Multiobjective Gate Assignment Model Considering Carbon Emissions
title_full_unstemmed Multiobjective Gate Assignment Model Considering Carbon Emissions
title_short Multiobjective Gate Assignment Model Considering Carbon Emissions
title_sort multiobjective gate assignment model considering carbon emissions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001770/
https://www.ncbi.nlm.nih.gov/pubmed/36900962
http://dx.doi.org/10.3390/ijerph20053952
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