An expert system to react to defective areas in nesting problems
Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bartmeyer, Petra Maria [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:209 ; year:2022 ; day:15 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.eswa.2022.118207 |
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| 520 | |a Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. | ||
| 520 | |a Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. | ||
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10.1016/j.eswa.2022.118207 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001928.pica (DE-627)ELV058949003 (ELSEVIER)S0957-4174(22)01363-X DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Bartmeyer, Petra Maria verfasserin aut An expert system to react to defective areas in nesting problems 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Strip-packing problem Elsevier Nesting problem Elsevier Transfer learning Elsevier Reinforcement learning Elsevier Heuristic Elsevier Oliveira, Larissa Tebaldi oth Leão, Aline Aparecida Souza oth Toledo, Franklina Maria Bragion oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:209 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.118207 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 209 2022 15 1215 0 |
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10.1016/j.eswa.2022.118207 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001928.pica (DE-627)ELV058949003 (ELSEVIER)S0957-4174(22)01363-X DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Bartmeyer, Petra Maria verfasserin aut An expert system to react to defective areas in nesting problems 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Strip-packing problem Elsevier Nesting problem Elsevier Transfer learning Elsevier Reinforcement learning Elsevier Heuristic Elsevier Oliveira, Larissa Tebaldi oth Leão, Aline Aparecida Souza oth Toledo, Franklina Maria Bragion oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:209 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.118207 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 209 2022 15 1215 0 |
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10.1016/j.eswa.2022.118207 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001928.pica (DE-627)ELV058949003 (ELSEVIER)S0957-4174(22)01363-X DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Bartmeyer, Petra Maria verfasserin aut An expert system to react to defective areas in nesting problems 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Strip-packing problem Elsevier Nesting problem Elsevier Transfer learning Elsevier Reinforcement learning Elsevier Heuristic Elsevier Oliveira, Larissa Tebaldi oth Leão, Aline Aparecida Souza oth Toledo, Franklina Maria Bragion oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:209 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.118207 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 209 2022 15 1215 0 |
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10.1016/j.eswa.2022.118207 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001928.pica (DE-627)ELV058949003 (ELSEVIER)S0957-4174(22)01363-X DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Bartmeyer, Petra Maria verfasserin aut An expert system to react to defective areas in nesting problems 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Strip-packing problem Elsevier Nesting problem Elsevier Transfer learning Elsevier Reinforcement learning Elsevier Heuristic Elsevier Oliveira, Larissa Tebaldi oth Leão, Aline Aparecida Souza oth Toledo, Franklina Maria Bragion oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:209 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.118207 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 209 2022 15 1215 0 |
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10.1016/j.eswa.2022.118207 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001928.pica (DE-627)ELV058949003 (ELSEVIER)S0957-4174(22)01363-X DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Bartmeyer, Petra Maria verfasserin aut An expert system to react to defective areas in nesting problems 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. Strip-packing problem Elsevier Nesting problem Elsevier Transfer learning Elsevier Reinforcement learning Elsevier Heuristic Elsevier Oliveira, Larissa Tebaldi oth Leão, Aline Aparecida Souza oth Toledo, Franklina Maria Bragion oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:209 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.eswa.2022.118207 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 209 2022 15 1215 0 |
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Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. |
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Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. |
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Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. |
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