Design and Analysis of Supply Chain Networks with Low Carbon Emissions
Abstract Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many f...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kuo, Tsai-Chi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Computational economics - Springer US, 1993, 52(2017), 4 vom: 13. Juli, Seite 1353-1374 |
|---|---|
| Übergeordnetes Werk: |
volume:52 ; year:2017 ; number:4 ; day:13 ; month:07 ; pages:1353-1374 |
| Links: |
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| DOI / URN: |
10.1007/s10614-017-9675-7 |
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OLC2027002774 |
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| 520 | |a Abstract Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously. | ||
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10.1007/s10614-017-9675-7 doi (DE-627)OLC2027002774 (DE-He213)s10614-017-9675-7-p DE-627 ger DE-627 rakwb eng 330 650 004 VZ 3,2 ssgn Kuo, Tsai-Chi verfasserin aut Design and Analysis of Supply Chain Networks with Low Carbon Emissions 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously. Greenhouse gas emission Low carbon supply chain networks Normal constraint method Tseng, Ming-Lang aut Chen, Hsiao-Min aut Chen, Ping-Shun aut Chang, Po-Chen aut Enthalten in Computational economics Springer US, 1993 52(2017), 4 vom: 13. Juli, Seite 1353-1374 (DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 0927-7099 nnns volume:52 year:2017 number:4 day:13 month:07 pages:1353-1374 https://doi.org/10.1007/s10614-017-9675-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 AR 52 2017 4 13 07 1353-1374 |
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Abstract Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously. © Springer Science+Business Media New York 2017 |
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Abstract Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously. © Springer Science+Business Media New York 2017 |
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Abstract Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously. © Springer Science+Business Media New York 2017 |
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10.1007/s10614-017-9675-7 |
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2024-07-03T13:21:40.703Z |
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