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Experimental dataset for optimising the freight rail operations

The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and t...

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Detalles Bibliográficos
Autores principales: Masoud, Mahmoud, Kozan, Erhan, Kent, Geoff, Liu, Shi Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5053071/
https://www.ncbi.nlm.nih.gov/pubmed/27747264
http://dx.doi.org/10.1016/j.dib.2016.09.015
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author Masoud, Mahmoud
Kozan, Erhan
Kent, Geoff
Liu, Shi Qiang
author_facet Masoud, Mahmoud
Kozan, Erhan
Kent, Geoff
Liu, Shi Qiang
author_sort Masoud, Mahmoud
collection PubMed
description The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E. Kozan, G. Kent, Liu, Shi Qiang, 2016b) [1]). Producing an efficient daily schedule to optimise the rail operations requires integration of the main elements of harvesting, transporting and milling in the value chain of the Australian agriculture industry. The data utilised in this research involve four main tables: sidings, harvesters, sectional rail network and trains. The utilised data were collected from Australian sugar mills as a real application. Operations Research techniques such as metaheuristic and constraint programming are used to produce the optimised solutions in an analytical way.
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spelling pubmed-50530712016-10-14 Experimental dataset for optimising the freight rail operations Masoud, Mahmoud Kozan, Erhan Kent, Geoff Liu, Shi Qiang Data Brief Data Article The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E. Kozan, G. Kent, Liu, Shi Qiang, 2016b) [1]). Producing an efficient daily schedule to optimise the rail operations requires integration of the main elements of harvesting, transporting and milling in the value chain of the Australian agriculture industry. The data utilised in this research involve four main tables: sidings, harvesters, sectional rail network and trains. The utilised data were collected from Australian sugar mills as a real application. Operations Research techniques such as metaheuristic and constraint programming are used to produce the optimised solutions in an analytical way. Elsevier 2016-09-21 /pmc/articles/PMC5053071/ /pubmed/27747264 http://dx.doi.org/10.1016/j.dib.2016.09.015 Text en © 2016 Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Masoud, Mahmoud
Kozan, Erhan
Kent, Geoff
Liu, Shi Qiang
Experimental dataset for optimising the freight rail operations
title Experimental dataset for optimising the freight rail operations
title_full Experimental dataset for optimising the freight rail operations
title_fullStr Experimental dataset for optimising the freight rail operations
title_full_unstemmed Experimental dataset for optimising the freight rail operations
title_short Experimental dataset for optimising the freight rail operations
title_sort experimental dataset for optimising the freight rail operations
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5053071/
https://www.ncbi.nlm.nih.gov/pubmed/27747264
http://dx.doi.org/10.1016/j.dib.2016.09.015
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