Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain
The farmed salmon supply chain has a highly complex and integrated structure, where activities occur both in the sea and on land. Due to this complexity, the supply chain needs appropriate decision-support tools to aid the production planning process, which capture the material flows, information fl...
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Online Access: | https://hdl.handle.net/11250/3026605 https://doi.org/10.3390/agriculture11100907 |
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ftsintef:oai:sintef.brage.unit.no:11250/3026605 2023-05-15T15:31:09+02:00 Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain Vempiliyath, Thomas Thakur, Maitri Hargaden, Vincent 2021 application/pdf https://hdl.handle.net/11250/3026605 https://doi.org/10.3390/agriculture11100907 eng eng MDPI Agriculture. 2021, 11 (10), 1-17. urn:issn:2077-0472 https://hdl.handle.net/11250/3026605 https://doi.org/10.3390/agriculture11100907 cristin:1997414 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no Copyright: © 2021 by the authors. 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/). CC-BY 17 11 Agriculture 10 hybrid simulation production planning discrete-event simulation (DES) agent-based modelling (ABM) farmed salmon supply chain Peer reviewed Journal article 2021 ftsintef https://doi.org/10.3390/agriculture11100907 2022-10-19T22:42:51Z The farmed salmon supply chain has a highly complex and integrated structure, where activities occur both in the sea and on land. Due to this complexity, the supply chain needs appropriate decision-support tools to aid the production planning process, which capture the material flows, information flows and behaviours of the decision makers in the chain. This paper proposes a hybrid simulation framework for production planning using the case of the Norwegian Atlantic salmon supply chain. This hybrid simulation comprises agent-based modelling (ABM) to capture the autonomous and interacting decision making behaviour of the supply chain actors, while discrete-event simulation (DES) is employed to model the various production processes within the chain. The simulation is implemented using AnyLogic™ version 8.0 simulation software, using a case study from the Norwegian farmed salmon sector. The proposed modelling framework provides a deeper understanding of the activities in the salmon supply chain, thereby enabling improved decision making. publishedVersion Article in Journal/Newspaper Atlantic salmon SINTEF Open (Brage) Agriculture 11 10 907 |
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Open Polar |
collection |
SINTEF Open (Brage) |
op_collection_id |
ftsintef |
language |
English |
topic |
hybrid simulation production planning discrete-event simulation (DES) agent-based modelling (ABM) farmed salmon supply chain |
spellingShingle |
hybrid simulation production planning discrete-event simulation (DES) agent-based modelling (ABM) farmed salmon supply chain Vempiliyath, Thomas Thakur, Maitri Hargaden, Vincent Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
topic_facet |
hybrid simulation production planning discrete-event simulation (DES) agent-based modelling (ABM) farmed salmon supply chain |
description |
The farmed salmon supply chain has a highly complex and integrated structure, where activities occur both in the sea and on land. Due to this complexity, the supply chain needs appropriate decision-support tools to aid the production planning process, which capture the material flows, information flows and behaviours of the decision makers in the chain. This paper proposes a hybrid simulation framework for production planning using the case of the Norwegian Atlantic salmon supply chain. This hybrid simulation comprises agent-based modelling (ABM) to capture the autonomous and interacting decision making behaviour of the supply chain actors, while discrete-event simulation (DES) is employed to model the various production processes within the chain. The simulation is implemented using AnyLogic™ version 8.0 simulation software, using a case study from the Norwegian farmed salmon sector. The proposed modelling framework provides a deeper understanding of the activities in the salmon supply chain, thereby enabling improved decision making. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Vempiliyath, Thomas Thakur, Maitri Hargaden, Vincent |
author_facet |
Vempiliyath, Thomas Thakur, Maitri Hargaden, Vincent |
author_sort |
Vempiliyath, Thomas |
title |
Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
title_short |
Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
title_full |
Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
title_fullStr |
Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
title_full_unstemmed |
Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
title_sort |
development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain |
publisher |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/11250/3026605 https://doi.org/10.3390/agriculture11100907 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_source |
17 11 Agriculture 10 |
op_relation |
Agriculture. 2021, 11 (10), 1-17. urn:issn:2077-0472 https://hdl.handle.net/11250/3026605 https://doi.org/10.3390/agriculture11100907 cristin:1997414 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no Copyright: © 2021 by the authors. 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/). |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/agriculture11100907 |
container_title |
Agriculture |
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11 |
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10 |
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907 |
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1766361640221016064 |