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Dataset for the electric capacitated traveling salesman problem

Regarding climate change and energy resource problems, cargo movement in the urban environment is essential to shift to a more sustainable mode. As cities seek to slash transport-related emissions and tackle traffic congestion, the cargo bike is showing itself to be an attractive and versatile last-...

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Detalles Bibliográficos
Autores principales: Gialos, Anastasios, Zeimpekis, Vasileios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430151/
https://www.ncbi.nlm.nih.gov/pubmed/37593182
http://dx.doi.org/10.1016/j.dib.2023.109464
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author Gialos, Anastasios
Zeimpekis, Vasileios
author_facet Gialos, Anastasios
Zeimpekis, Vasileios
author_sort Gialos, Anastasios
collection PubMed
description Regarding climate change and energy resource problems, cargo movement in the urban environment is essential to shift to a more sustainable mode. As cities seek to slash transport-related emissions and tackle traffic congestion, the cargo bike is showing itself to be an attractive and versatile last-mile delivery alternative. To this end, this article presents a series of datasets for the Electric Capacitated Travelling Salesman Problem (EC-TSP). This problem has been built for modeling and solving the e-cargo bike parcel distribution problem in urban environments. For the design of these datasets, real geographical data have been used that are in the city centers of Athens, Thessaloniki, Patra, and Larisa in Greece. These datasets have been used for the assessment of e-cargo bikes versus typical delivery vans in terms of operational efficiency and CO(2)e emissions. The entire benchmark is composed of 9 instances comprised of 14–29 nodes each. The datasets are publicly available for use and modification.
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spelling pubmed-104301512023-08-17 Dataset for the electric capacitated traveling salesman problem Gialos, Anastasios Zeimpekis, Vasileios Data Brief Data Article Regarding climate change and energy resource problems, cargo movement in the urban environment is essential to shift to a more sustainable mode. As cities seek to slash transport-related emissions and tackle traffic congestion, the cargo bike is showing itself to be an attractive and versatile last-mile delivery alternative. To this end, this article presents a series of datasets for the Electric Capacitated Travelling Salesman Problem (EC-TSP). This problem has been built for modeling and solving the e-cargo bike parcel distribution problem in urban environments. For the design of these datasets, real geographical data have been used that are in the city centers of Athens, Thessaloniki, Patra, and Larisa in Greece. These datasets have been used for the assessment of e-cargo bikes versus typical delivery vans in terms of operational efficiency and CO(2)e emissions. The entire benchmark is composed of 9 instances comprised of 14–29 nodes each. The datasets are publicly available for use and modification. Elsevier 2023-08-01 /pmc/articles/PMC10430151/ /pubmed/37593182 http://dx.doi.org/10.1016/j.dib.2023.109464 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Gialos, Anastasios
Zeimpekis, Vasileios
Dataset for the electric capacitated traveling salesman problem
title Dataset for the electric capacitated traveling salesman problem
title_full Dataset for the electric capacitated traveling salesman problem
title_fullStr Dataset for the electric capacitated traveling salesman problem
title_full_unstemmed Dataset for the electric capacitated traveling salesman problem
title_short Dataset for the electric capacitated traveling salesman problem
title_sort dataset for the electric capacitated traveling salesman problem
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430151/
https://www.ncbi.nlm.nih.gov/pubmed/37593182
http://dx.doi.org/10.1016/j.dib.2023.109464
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