Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm
Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fu, Yaping [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
Multi-objective multi-agent scheduling |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 30(2018), 5 vom: 06. Jan., Seite 2257-2272 |
|---|---|
| Übergeordnetes Werk: |
volume:30 ; year:2018 ; number:5 ; day:06 ; month:01 ; pages:2257-2272 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10845-017-1385-4 |
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OLC2066779008 |
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| 520 | |a Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. | ||
| 650 | 4 | |a Flow shop scheduling | |
| 650 | 4 | |a Deteriorating scheduling | |
| 650 | 4 | |a Multi-objective multi-agent scheduling | |
| 650 | 4 | |a Multi-objective evolutionary algorithm | |
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| 700 | 1 | |a Wang, Hongfeng |4 aut | |
| 700 | 1 | |a Tian, Guangdong |4 aut | |
| 700 | 1 | |a Li, Zhiwu |4 aut | |
| 700 | 1 | |a Hu, Hesuan |4 aut | |
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10.1007/s10845-017-1385-4 doi (DE-627)OLC2066779008 (DE-He213)s10845-017-1385-4-p DE-627 ger DE-627 rakwb eng 620 004 VZ Fu, Yaping verfasserin aut Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. Flow shop scheduling Deteriorating scheduling Multi-objective multi-agent scheduling Multi-objective evolutionary algorithm Multipopulation Wang, Hongfeng aut Tian, Guangdong aut Li, Zhiwu aut Hu, Hesuan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2018), 5 vom: 06. Jan., Seite 2257-2272 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2018 number:5 day:06 month:01 pages:2257-2272 https://doi.org/10.1007/s10845-017-1385-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2018 5 06 01 2257-2272 |
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10.1007/s10845-017-1385-4 doi (DE-627)OLC2066779008 (DE-He213)s10845-017-1385-4-p DE-627 ger DE-627 rakwb eng 620 004 VZ Fu, Yaping verfasserin aut Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. Flow shop scheduling Deteriorating scheduling Multi-objective multi-agent scheduling Multi-objective evolutionary algorithm Multipopulation Wang, Hongfeng aut Tian, Guangdong aut Li, Zhiwu aut Hu, Hesuan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2018), 5 vom: 06. Jan., Seite 2257-2272 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2018 number:5 day:06 month:01 pages:2257-2272 https://doi.org/10.1007/s10845-017-1385-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2018 5 06 01 2257-2272 |
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10.1007/s10845-017-1385-4 doi (DE-627)OLC2066779008 (DE-He213)s10845-017-1385-4-p DE-627 ger DE-627 rakwb eng 620 004 VZ Fu, Yaping verfasserin aut Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. Flow shop scheduling Deteriorating scheduling Multi-objective multi-agent scheduling Multi-objective evolutionary algorithm Multipopulation Wang, Hongfeng aut Tian, Guangdong aut Li, Zhiwu aut Hu, Hesuan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2018), 5 vom: 06. Jan., Seite 2257-2272 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2018 number:5 day:06 month:01 pages:2257-2272 https://doi.org/10.1007/s10845-017-1385-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2018 5 06 01 2257-2272 |
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10.1007/s10845-017-1385-4 doi (DE-627)OLC2066779008 (DE-He213)s10845-017-1385-4-p DE-627 ger DE-627 rakwb eng 620 004 VZ Fu, Yaping verfasserin aut Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. Flow shop scheduling Deteriorating scheduling Multi-objective multi-agent scheduling Multi-objective evolutionary algorithm Multipopulation Wang, Hongfeng aut Tian, Guangdong aut Li, Zhiwu aut Hu, Hesuan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2018), 5 vom: 06. Jan., Seite 2257-2272 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2018 number:5 day:06 month:01 pages:2257-2272 https://doi.org/10.1007/s10845-017-1385-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2018 5 06 01 2257-2272 |
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Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm |
| abstract |
Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| title_short |
Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm |
| url |
https://doi.org/10.1007/s10845-017-1385-4 |
| remote_bool |
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| author2 |
Wang, Hongfeng Tian, Guangdong Li, Zhiwu Hu, Hesuan |
| author2Str |
Wang, Hongfeng Tian, Guangdong Li, Zhiwu Hu, Hesuan |
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| doi_str |
10.1007/s10845-017-1385-4 |
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2024-07-04T05:16:48.975Z |
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