Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model
The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integra...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhuo Dai [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computers & industrial engineering - Amsterdam [u.a.] : Elsevier, 1976, 88(2015), Seite 444 |
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| Übergeordnetes Werk: |
volume:88 ; year:2015 ; pages:444 |
| Links: |
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| DOI / URN: |
10.1016/j.cie.2015.08.004 |
|---|
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| 520 | |a The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. | ||
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| 650 | 4 | |a Supply chain management | |
| 650 | 4 | |a Uncertainty | |
| 650 | 4 | |a Sensitivity analysis | |
| 650 | 4 | |a Studies | |
| 650 | 4 | |a Closed loop systems | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Algorithms | |
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10.1016/j.cie.2015.08.004 doi PQ20160617 (DE-627)OLC1963023722 (DE-599)GBVOLC1963023722 (PRQ)c2414-834f43a1e3c6a87f827d88b37082a336bd3ff2f0638fb7175254f19dcd04c7db0 (KEY)0011885020150000088000000444designofcloseloopsupplychainnetworkunderuncertaint DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Zhuo Dai verfasserin aut Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Monte Carlo simulation Supply chain management Uncertainty Sensitivity analysis Studies Closed loop systems Genetic algorithms Algorithms Monte Carlo method Logistics Xiaoting Zheng oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 444 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:444 http://dx.doi.org/10.1016/j.cie.2015.08.004 Volltext http://search.proquest.com/docview/1713974449 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 444 |
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10.1016/j.cie.2015.08.004 doi PQ20160617 (DE-627)OLC1963023722 (DE-599)GBVOLC1963023722 (PRQ)c2414-834f43a1e3c6a87f827d88b37082a336bd3ff2f0638fb7175254f19dcd04c7db0 (KEY)0011885020150000088000000444designofcloseloopsupplychainnetworkunderuncertaint DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Zhuo Dai verfasserin aut Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Monte Carlo simulation Supply chain management Uncertainty Sensitivity analysis Studies Closed loop systems Genetic algorithms Algorithms Monte Carlo method Logistics Xiaoting Zheng oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 444 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:444 http://dx.doi.org/10.1016/j.cie.2015.08.004 Volltext http://search.proquest.com/docview/1713974449 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 444 |
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10.1016/j.cie.2015.08.004 doi PQ20160617 (DE-627)OLC1963023722 (DE-599)GBVOLC1963023722 (PRQ)c2414-834f43a1e3c6a87f827d88b37082a336bd3ff2f0638fb7175254f19dcd04c7db0 (KEY)0011885020150000088000000444designofcloseloopsupplychainnetworkunderuncertaint DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Zhuo Dai verfasserin aut Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Monte Carlo simulation Supply chain management Uncertainty Sensitivity analysis Studies Closed loop systems Genetic algorithms Algorithms Monte Carlo method Logistics Xiaoting Zheng oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 444 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:444 http://dx.doi.org/10.1016/j.cie.2015.08.004 Volltext http://search.proquest.com/docview/1713974449 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 444 |
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10.1016/j.cie.2015.08.004 doi PQ20160617 (DE-627)OLC1963023722 (DE-599)GBVOLC1963023722 (PRQ)c2414-834f43a1e3c6a87f827d88b37082a336bd3ff2f0638fb7175254f19dcd04c7db0 (KEY)0011885020150000088000000444designofcloseloopsupplychainnetworkunderuncertaint DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Zhuo Dai verfasserin aut Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Monte Carlo simulation Supply chain management Uncertainty Sensitivity analysis Studies Closed loop systems Genetic algorithms Algorithms Monte Carlo method Logistics Xiaoting Zheng oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 444 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:444 http://dx.doi.org/10.1016/j.cie.2015.08.004 Volltext http://search.proquest.com/docview/1713974449 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 444 |
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10.1016/j.cie.2015.08.004 doi PQ20160617 (DE-627)OLC1963023722 (DE-599)GBVOLC1963023722 (PRQ)c2414-834f43a1e3c6a87f827d88b37082a336bd3ff2f0638fb7175254f19dcd04c7db0 (KEY)0011885020150000088000000444designofcloseloopsupplychainnetworkunderuncertaint DE-627 ger DE-627 rakwb eng 004 DNB 85.35 bkl 54.80 bkl 83.00 bkl Zhuo Dai verfasserin aut Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. Nutzungsrecht: © COPYRIGHT 2015 Elsevier Science Publishers Monte Carlo simulation Supply chain management Uncertainty Sensitivity analysis Studies Closed loop systems Genetic algorithms Algorithms Monte Carlo method Logistics Xiaoting Zheng oth Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier, 1976 88(2015), Seite 444 (DE-627)129448982 (DE-600)196993-6 (DE-576)014814994 0360-8352 nnns volume:88 year:2015 pages:444 http://dx.doi.org/10.1016/j.cie.2015.08.004 Volltext http://search.proquest.com/docview/1713974449 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_70 85.35 AVZ 54.80 AVZ 83.00 AVZ AR 88 2015 444 |
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Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model |
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Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model |
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Zhuo Dai |
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design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: a fuzzy and chance-constrained programming model |
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Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model |
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The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. |
| abstractGer |
The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. |
| abstract_unstemmed |
The design of closed-loop supply chain network is one of the important issues in supply chain management. This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty. Because of its complexity, a solution framework which integrates Monte Carlo simulation embedded hybrid genetic algorithm, fuzzy programming and chance-constrained programming jointly deal with the issue. A fuzzy programming and chance-constrained programming approach take up the uncertainty issue. Monte Carlo simulation embedded hybrid genetic algorithm is employed to determine the configuration of CLSC network. Parameters of GA are chosen to balance two aims. One aim is that the best value is global optimum, that is, maximum profit. The other aim is that the computational time is as short as possible. Non-parametric test confirms the advantage of hybrid GA. Then, the validity of Monte Carlo simulation embedded hybrid genetic algorithm is verified. The impacts of uncertainty in disposed rates, demands, and capacities on the overall profit of CLSC network are studied through sensitivity analysis. The proposed model is effective in designing CLSC network under uncertain environment. |
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Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model |
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http://dx.doi.org/10.1016/j.cie.2015.08.004 http://search.proquest.com/docview/1713974449 |
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