A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning
Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) st...
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Gespeichert in:
| Autor*in: |
Yang, S. S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Shanghai University and Springer-Verlag Berlin Heidelberg 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Advances in manufacturing - Berlin : Springer, 2013, 4(2016), 3 vom: 05. Aug., Seite 189-201 |
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| Übergeordnetes Werk: |
volume:4 ; year:2016 ; number:3 ; day:05 ; month:08 ; pages:189-201 |
| Links: |
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| DOI / URN: |
10.1007/s40436-016-0149-2 |
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| Katalog-ID: |
SPR036477699 |
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| 520 | |a Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. | ||
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10.1007/s40436-016-0149-2 doi (DE-627)SPR036477699 (SPR)s40436-016-0149-2-e DE-627 ger DE-627 rakwb eng Yang, S. S. verfasserin aut A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. Sustainable manufacturing (dpeaa)DE-He213 Remanufacturing (dpeaa)DE-He213 Recycling (dpeaa)DE-He213 End-of-life (EOL) (dpeaa)DE-He213 Decision-making model (dpeaa)DE-He213 Nasr, N. aut Ong, S. K. aut Nee, A. Y. C. aut Enthalten in Advances in manufacturing Berlin : Springer, 2013 4(2016), 3 vom: 05. Aug., Seite 189-201 (DE-627)742224732 (DE-600)2711809-5 2195-3597 nnns volume:4 year:2016 number:3 day:05 month:08 pages:189-201 https://dx.doi.org/10.1007/s40436-016-0149-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 4 2016 3 05 08 189-201 |
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10.1007/s40436-016-0149-2 doi (DE-627)SPR036477699 (SPR)s40436-016-0149-2-e DE-627 ger DE-627 rakwb eng Yang, S. S. verfasserin aut A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. Sustainable manufacturing (dpeaa)DE-He213 Remanufacturing (dpeaa)DE-He213 Recycling (dpeaa)DE-He213 End-of-life (EOL) (dpeaa)DE-He213 Decision-making model (dpeaa)DE-He213 Nasr, N. aut Ong, S. K. aut Nee, A. Y. C. aut Enthalten in Advances in manufacturing Berlin : Springer, 2013 4(2016), 3 vom: 05. Aug., Seite 189-201 (DE-627)742224732 (DE-600)2711809-5 2195-3597 nnns volume:4 year:2016 number:3 day:05 month:08 pages:189-201 https://dx.doi.org/10.1007/s40436-016-0149-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 4 2016 3 05 08 189-201 |
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10.1007/s40436-016-0149-2 doi (DE-627)SPR036477699 (SPR)s40436-016-0149-2-e DE-627 ger DE-627 rakwb eng Yang, S. S. verfasserin aut A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. Sustainable manufacturing (dpeaa)DE-He213 Remanufacturing (dpeaa)DE-He213 Recycling (dpeaa)DE-He213 End-of-life (EOL) (dpeaa)DE-He213 Decision-making model (dpeaa)DE-He213 Nasr, N. aut Ong, S. K. aut Nee, A. Y. C. aut Enthalten in Advances in manufacturing Berlin : Springer, 2013 4(2016), 3 vom: 05. Aug., Seite 189-201 (DE-627)742224732 (DE-600)2711809-5 2195-3597 nnns volume:4 year:2016 number:3 day:05 month:08 pages:189-201 https://dx.doi.org/10.1007/s40436-016-0149-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 4 2016 3 05 08 189-201 |
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10.1007/s40436-016-0149-2 doi (DE-627)SPR036477699 (SPR)s40436-016-0149-2-e DE-627 ger DE-627 rakwb eng Yang, S. S. verfasserin aut A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. Sustainable manufacturing (dpeaa)DE-He213 Remanufacturing (dpeaa)DE-He213 Recycling (dpeaa)DE-He213 End-of-life (EOL) (dpeaa)DE-He213 Decision-making model (dpeaa)DE-He213 Nasr, N. aut Ong, S. K. aut Nee, A. Y. C. aut Enthalten in Advances in manufacturing Berlin : Springer, 2013 4(2016), 3 vom: 05. Aug., Seite 189-201 (DE-627)742224732 (DE-600)2711809-5 2195-3597 nnns volume:4 year:2016 number:3 day:05 month:08 pages:189-201 https://dx.doi.org/10.1007/s40436-016-0149-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 4 2016 3 05 08 189-201 |
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10.1007/s40436-016-0149-2 doi (DE-627)SPR036477699 (SPR)s40436-016-0149-2-e DE-627 ger DE-627 rakwb eng Yang, S. S. verfasserin aut A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. Sustainable manufacturing (dpeaa)DE-He213 Remanufacturing (dpeaa)DE-He213 Recycling (dpeaa)DE-He213 End-of-life (EOL) (dpeaa)DE-He213 Decision-making model (dpeaa)DE-He213 Nasr, N. aut Ong, S. K. aut Nee, A. Y. C. aut Enthalten in Advances in manufacturing Berlin : Springer, 2013 4(2016), 3 vom: 05. Aug., Seite 189-201 (DE-627)742224732 (DE-600)2711809-5 2195-3597 nnns volume:4 year:2016 number:3 day:05 month:08 pages:189-201 https://dx.doi.org/10.1007/s40436-016-0149-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 4 2016 3 05 08 189-201 |
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S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Shanghai University and Springer-Verlag Berlin Heidelberg 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. 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| author |
Yang, S. S. |
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Yang, S. S. misc Sustainable manufacturing misc Remanufacturing misc Recycling misc End-of-life (EOL) misc Decision-making model A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning |
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A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning Sustainable manufacturing (dpeaa)DE-He213 Remanufacturing (dpeaa)DE-He213 Recycling (dpeaa)DE-He213 End-of-life (EOL) (dpeaa)DE-He213 Decision-making model (dpeaa)DE-He213 |
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A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning |
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Yang, S. S. |
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holistic decision support tool for remanufacturing: end-of-life (eol) strategy planning |
| title_auth |
A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning |
| abstract |
Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 |
| abstractGer |
Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 |
| abstract_unstemmed |
Abstract Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology. © Shanghai University and Springer-Verlag Berlin Heidelberg 2016 |
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A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning |
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https://dx.doi.org/10.1007/s40436-016-0149-2 |
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Nasr, N. Ong, S. K. Nee, A. Y. C. |
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Nasr, N. Ong, S. K. Nee, A. Y. C. |
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10.1007/s40436-016-0149-2 |
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2024-07-03T17:51:44.265Z |
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| score |
7.402587 |