Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty
As China’s "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads t...
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crplos:10.1371/journal.pone.0297223 2024-05-19T07:32:46+00:00 Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty Zhu, Yuhua Fan, Xiang Yin, Chuanzhong Fathollahi-Fard, Amir M. Shanghai Philosophy and Social Science Planning Project Humanities and Social Sciences Foundation of the Ministry of Education of the People’s Republic of China Research Program Project of China Railway Shanghai Bureau Group Co., Ltd 2024 http://dx.doi.org/10.1371/journal.pone.0297223 https://dx.plos.org/10.1371/journal.pone.0297223 en eng Public Library of Science (PLoS) http://creativecommons.org/licenses/by/4.0/ PLOS ONE volume 19, issue 1, page e0297223 ISSN 1932-6203 journal-article 2024 crplos https://doi.org/10.1371/journal.pone.0297223 2024-05-01T07:07:02Z As China’s "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0–1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency. Article in Journal/Newspaper Aptenodytes forsteri PLOS PLOS ONE 19 1 e0297223 |
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English |
description |
As China’s "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0–1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency. |
author2 |
Fathollahi-Fard, Amir M. Shanghai Philosophy and Social Science Planning Project Humanities and Social Sciences Foundation of the Ministry of Education of the People’s Republic of China Research Program Project of China Railway Shanghai Bureau Group Co., Ltd |
format |
Article in Journal/Newspaper |
author |
Zhu, Yuhua Fan, Xiang Yin, Chuanzhong |
spellingShingle |
Zhu, Yuhua Fan, Xiang Yin, Chuanzhong Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
author_facet |
Zhu, Yuhua Fan, Xiang Yin, Chuanzhong |
author_sort |
Zhu, Yuhua |
title |
Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
title_short |
Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
title_full |
Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
title_fullStr |
Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
title_full_unstemmed |
Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
title_sort |
aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty |
publisher |
Public Library of Science (PLoS) |
publishDate |
2024 |
url |
http://dx.doi.org/10.1371/journal.pone.0297223 https://dx.plos.org/10.1371/journal.pone.0297223 |
genre |
Aptenodytes forsteri |
genre_facet |
Aptenodytes forsteri |
op_source |
PLOS ONE volume 19, issue 1, page e0297223 ISSN 1932-6203 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1371/journal.pone.0297223 |
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PLOS ONE |
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19 |
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e0297223 |
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