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] Tseng, Ming-Lang [verfasserIn] Chen, Hsiao-Min [verfasserIn] Chen, Ping-Shun [verfasserIn] Chang, Po-Chen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computational economics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 52(2017), 4 vom: 13. Juli, Seite 1353-1374 |
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| 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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| Katalog-ID: |
SPR011776706 |
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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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| 700 | 1 | |a Chen, Ping-Shun |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Po-Chen |e verfasserin |4 aut | |
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10.1007/s10614-017-9675-7 doi (DE-627)SPR011776706 (SPR)s10614-017-9675-7-e DE-627 ger DE-627 rakwb eng 330 650 004 ASE 85.03 bkl 31.80 bkl 54.10 bkl Kuo, Tsai-Chi verfasserin aut Design and Analysis of Supply Chain Networks with Low Carbon Emissions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Low carbon supply chain networks (dpeaa)DE-He213 Normal constraint method (dpeaa)DE-He213 Tseng, Ming-Lang verfasserin aut Chen, Hsiao-Min verfasserin aut Chen, Ping-Shun verfasserin aut Chang, Po-Chen verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 52(2017), 4 vom: 13. Juli, Seite 1353-1374 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:52 year:2017 number:4 day:13 month:07 pages:1353-1374 https://dx.doi.org/10.1007/s10614-017-9675-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE 31.80 ASE 54.10 ASE AR 52 2017 4 13 07 1353-1374 |
| spelling |
10.1007/s10614-017-9675-7 doi (DE-627)SPR011776706 (SPR)s10614-017-9675-7-e DE-627 ger DE-627 rakwb eng 330 650 004 ASE 85.03 bkl 31.80 bkl 54.10 bkl Kuo, Tsai-Chi verfasserin aut Design and Analysis of Supply Chain Networks with Low Carbon Emissions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Low carbon supply chain networks (dpeaa)DE-He213 Normal constraint method (dpeaa)DE-He213 Tseng, Ming-Lang verfasserin aut Chen, Hsiao-Min verfasserin aut Chen, Ping-Shun verfasserin aut Chang, Po-Chen verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 52(2017), 4 vom: 13. Juli, Seite 1353-1374 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:52 year:2017 number:4 day:13 month:07 pages:1353-1374 https://dx.doi.org/10.1007/s10614-017-9675-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE 31.80 ASE 54.10 ASE AR 52 2017 4 13 07 1353-1374 |
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10.1007/s10614-017-9675-7 doi (DE-627)SPR011776706 (SPR)s10614-017-9675-7-e DE-627 ger DE-627 rakwb eng 330 650 004 ASE 85.03 bkl 31.80 bkl 54.10 bkl Kuo, Tsai-Chi verfasserin aut Design and Analysis of Supply Chain Networks with Low Carbon Emissions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Low carbon supply chain networks (dpeaa)DE-He213 Normal constraint method (dpeaa)DE-He213 Tseng, Ming-Lang verfasserin aut Chen, Hsiao-Min verfasserin aut Chen, Ping-Shun verfasserin aut Chang, Po-Chen verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 52(2017), 4 vom: 13. Juli, Seite 1353-1374 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:52 year:2017 number:4 day:13 month:07 pages:1353-1374 https://dx.doi.org/10.1007/s10614-017-9675-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE 31.80 ASE 54.10 ASE AR 52 2017 4 13 07 1353-1374 |
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10.1007/s10614-017-9675-7 doi (DE-627)SPR011776706 (SPR)s10614-017-9675-7-e DE-627 ger DE-627 rakwb eng 330 650 004 ASE 85.03 bkl 31.80 bkl 54.10 bkl Kuo, Tsai-Chi verfasserin aut Design and Analysis of Supply Chain Networks with Low Carbon Emissions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Low carbon supply chain networks (dpeaa)DE-He213 Normal constraint method (dpeaa)DE-He213 Tseng, Ming-Lang verfasserin aut Chen, Hsiao-Min verfasserin aut Chen, Ping-Shun verfasserin aut Chang, Po-Chen verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 52(2017), 4 vom: 13. Juli, Seite 1353-1374 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:52 year:2017 number:4 day:13 month:07 pages:1353-1374 https://dx.doi.org/10.1007/s10614-017-9675-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE 31.80 ASE 54.10 ASE AR 52 2017 4 13 07 1353-1374 |
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10.1007/s10614-017-9675-7 doi (DE-627)SPR011776706 (SPR)s10614-017-9675-7-e DE-627 ger DE-627 rakwb eng 330 650 004 ASE 85.03 bkl 31.80 bkl 54.10 bkl Kuo, Tsai-Chi verfasserin aut Design and Analysis of Supply Chain Networks with Low Carbon Emissions 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Low carbon supply chain networks (dpeaa)DE-He213 Normal constraint method (dpeaa)DE-He213 Tseng, Ming-Lang verfasserin aut Chen, Hsiao-Min verfasserin aut Chen, Ping-Shun verfasserin aut Chang, Po-Chen verfasserin aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 52(2017), 4 vom: 13. Juli, Seite 1353-1374 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:52 year:2017 number:4 day:13 month:07 pages:1353-1374 https://dx.doi.org/10.1007/s10614-017-9675-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE 31.80 ASE 54.10 ASE AR 52 2017 4 13 07 1353-1374 |
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Kuo, Tsai-Chi @@aut@@ Tseng, Ming-Lang @@aut@@ Chen, Hsiao-Min @@aut@@ Chen, Ping-Shun @@aut@@ Chang, Po-Chen @@aut@@ |
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Kuo, Tsai-Chi |
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330 650 004 ASE 85.03 bkl 31.80 bkl 54.10 bkl Design and Analysis of Supply Chain Networks with Low Carbon Emissions Greenhouse gas emission (dpeaa)DE-He213 Low carbon supply chain networks (dpeaa)DE-He213 Normal constraint method (dpeaa)DE-He213 |
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Design and Analysis of Supply Chain Networks with Low Carbon Emissions |
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Kuo, Tsai-Chi Tseng, Ming-Lang Chen, Hsiao-Min Chen, Ping-Shun Chang, Po-Chen |
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design and analysis of supply chain networks with low carbon emissions |
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Design and Analysis of Supply Chain Networks with Low Carbon Emissions |
| abstract |
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. |
| abstractGer |
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. |
| abstract_unstemmed |
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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| title_short |
Design and Analysis of Supply Chain Networks with Low Carbon Emissions |
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https://dx.doi.org/10.1007/s10614-017-9675-7 |
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| author2 |
Tseng, Ming-Lang Chen, Hsiao-Min Chen, Ping-Shun Chang, Po-Chen |
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Tseng, Ming-Lang Chen, Hsiao-Min Chen, Ping-Shun Chang, Po-Chen |
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| doi_str |
10.1007/s10614-017-9675-7 |
| up_date |
2024-07-04T00:21:37.182Z |
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| score |
7.399889 |