Research on a collaboration model of green closed-loop supply chains towards intelligent manufacturing
Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip eff...
Ausführliche Beschreibung
Autor*in: |
Qi, Jin [verfasserIn] |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 28 vom: 12. Mai, Seite 40609-40634 |
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Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:28 ; day:12 ; month:05 ; pages:40609-40634 |
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DOI / URN: |
10.1007/s11042-021-11727-w |
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OLC2079837850 |
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520 | |a Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. | ||
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10.1007/s11042-021-11727-w doi (DE-627)OLC2079837850 (DE-He213)s11042-021-11727-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Qi, Jin verfasserin aut Research on a collaboration model of green closed-loop supply chains towards intelligent manufacturing 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. Intelligent manufacturing Deep Q-learning Closed-loop supply chain Remanufacturing Ling, Yaochen aut Ji, Binglong aut Liu, Yali aut Shen, Zixin aut Xu, Bin aut Xue, Yu aut Sun, Yanfei (orcid)0000-0003-0085-1545 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 28 vom: 12. Mai, Seite 40609-40634 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:28 day:12 month:05 pages:40609-40634 https://doi.org/10.1007/s11042-021-11727-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 28 12 05 40609-40634 |
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10.1007/s11042-021-11727-w doi (DE-627)OLC2079837850 (DE-He213)s11042-021-11727-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Qi, Jin verfasserin aut Research on a collaboration model of green closed-loop supply chains towards intelligent manufacturing 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. Intelligent manufacturing Deep Q-learning Closed-loop supply chain Remanufacturing Ling, Yaochen aut Ji, Binglong aut Liu, Yali aut Shen, Zixin aut Xu, Bin aut Xue, Yu aut Sun, Yanfei (orcid)0000-0003-0085-1545 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 28 vom: 12. Mai, Seite 40609-40634 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:28 day:12 month:05 pages:40609-40634 https://doi.org/10.1007/s11042-021-11727-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 28 12 05 40609-40634 |
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10.1007/s11042-021-11727-w doi (DE-627)OLC2079837850 (DE-He213)s11042-021-11727-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Qi, Jin verfasserin aut Research on a collaboration model of green closed-loop supply chains towards intelligent manufacturing 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. Intelligent manufacturing Deep Q-learning Closed-loop supply chain Remanufacturing Ling, Yaochen aut Ji, Binglong aut Liu, Yali aut Shen, Zixin aut Xu, Bin aut Xue, Yu aut Sun, Yanfei (orcid)0000-0003-0085-1545 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 28 vom: 12. Mai, Seite 40609-40634 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:28 day:12 month:05 pages:40609-40634 https://doi.org/10.1007/s11042-021-11727-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 28 12 05 40609-40634 |
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Research on a collaboration model of green closed-loop supply chains towards intelligent manufacturing |
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Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
abstractGer |
Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
abstract_unstemmed |
Abstract A closed-loop supply chain (CLSC) is a complete supply chain cycle that closes the flow of logistics from procurement to sales to reduce pollution and optimize returns. In this paper, we aim to address the problems of uncertain supply and demand, environmental pollution and the bullwhip effect in CLSCs. This paper constructs a green CLSC collaboration model with intelligent manufacturing and proposes a neural fictitious self-play (NFSP) algorithm based on reinforcement learning. A complete green CLSC collaboration model is constructed by modeling and analyzing indicators such as recycling prices and price sensitivity in the intelligent manufacturing supply chain, and dual-channel independent recycling and green dual-channel hybrid models are considered. Then, the ADMM algorithm is used in advance to solve the initial value of the mixed model, and the NFSP algorithm based on deep Q-learning is proposed to solve the green CLSC collaboration model. Studies have shown that the green CLSC collaboration model with intelligent manufacturing is an environmentally friendly solution that closely connects recyclers and retailers: it is an intelligent and efficient supply chain model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
collection_details |
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container_issue |
28 |
title_short |
Research on a collaboration model of green closed-loop supply chains towards intelligent manufacturing |
url |
https://doi.org/10.1007/s11042-021-11727-w |
remote_bool |
false |
author2 |
Ling, Yaochen Ji, Binglong Liu, Yali Shen, Zixin Xu, Bin Xue, Yu Sun, Yanfei |
author2Str |
Ling, Yaochen Ji, Binglong Liu, Yali Shen, Zixin Xu, Bin Xue, Yu Sun, Yanfei |
ppnlink |
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hochschulschrift_bool |
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doi_str |
10.1007/s11042-021-11727-w |
up_date |
2024-07-04T02:11:08.697Z |
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