Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor
ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The...
Ausführliche Beschreibung
Autor*in: |
Menanno Marialuisa [verfasserIn] Savino Matteo M. [verfasserIn] Shafiq Muhammad [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Production and Manufacturing Research: An Open Access Journal - Taylor & Francis Group, 2015, 11(2023), 1 |
---|---|
Übergeordnetes Werk: |
volume:11 ; year:2023 ; number:1 |
Links: |
---|
DOI / URN: |
10.1080/21693277.2023.2179123 |
---|
Katalog-ID: |
DOAJ081062206 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ081062206 | ||
003 | DE-627 | ||
005 | 20240414201644.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230310s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1080/21693277.2023.2179123 |2 doi | |
035 | |a (DE-627)DOAJ081062206 | ||
035 | |a (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TS1-2301 | |
050 | 0 | |a HF5001-6182 | |
100 | 0 | |a Menanno Marialuisa |e verfasserin |4 aut | |
245 | 1 | 0 | |a Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. | ||
650 | 4 | |a Milk-run system (MRS) | |
650 | 4 | |a IT-based Kanban (ITK) | |
650 | 4 | |a artificial intelligence | |
650 | 4 | |a multi-lines assembly shop floor | |
653 | 0 | |a Technology | |
653 | 0 | |a T | |
653 | 0 | |a Manufactures | |
653 | 0 | |a Business | |
700 | 0 | |a Savino Matteo M. |e verfasserin |4 aut | |
700 | 0 | |a Shafiq Muhammad |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Production and Manufacturing Research: An Open Access Journal |d Taylor & Francis Group, 2015 |g 11(2023), 1 |w (DE-627)769570496 |w (DE-600)2735819-7 |x 21693277 |7 nnns |
773 | 1 | 8 | |g volume:11 |g year:2023 |g number:1 |
856 | 4 | 0 | |u https://doi.org/10.1080/21693277.2023.2179123 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c |z kostenfrei |
856 | 4 | 0 | |u https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2169-3277 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 11 |j 2023 |e 1 |
author_variant |
m m mm s m m smm s m sm |
---|---|
matchkey_str |
article:21693277:2023----::piiigikusseadtaeknawtatfcaitliecaeprcltd |
hierarchy_sort_str |
2023 |
callnumber-subject-code |
TS |
publishDate |
2023 |
allfields |
10.1080/21693277.2023.2179123 doi (DE-627)DOAJ081062206 (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c DE-627 ger DE-627 rakwb eng TS1-2301 HF5001-6182 Menanno Marialuisa verfasserin aut Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor Technology T Manufactures Business Savino Matteo M. verfasserin aut Shafiq Muhammad verfasserin aut In Production and Manufacturing Research: An Open Access Journal Taylor & Francis Group, 2015 11(2023), 1 (DE-627)769570496 (DE-600)2735819-7 21693277 nnns volume:11 year:2023 number:1 https://doi.org/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c kostenfrei https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/toc/2169-3277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 |
spelling |
10.1080/21693277.2023.2179123 doi (DE-627)DOAJ081062206 (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c DE-627 ger DE-627 rakwb eng TS1-2301 HF5001-6182 Menanno Marialuisa verfasserin aut Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor Technology T Manufactures Business Savino Matteo M. verfasserin aut Shafiq Muhammad verfasserin aut In Production and Manufacturing Research: An Open Access Journal Taylor & Francis Group, 2015 11(2023), 1 (DE-627)769570496 (DE-600)2735819-7 21693277 nnns volume:11 year:2023 number:1 https://doi.org/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c kostenfrei https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/toc/2169-3277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 |
allfields_unstemmed |
10.1080/21693277.2023.2179123 doi (DE-627)DOAJ081062206 (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c DE-627 ger DE-627 rakwb eng TS1-2301 HF5001-6182 Menanno Marialuisa verfasserin aut Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor Technology T Manufactures Business Savino Matteo M. verfasserin aut Shafiq Muhammad verfasserin aut In Production and Manufacturing Research: An Open Access Journal Taylor & Francis Group, 2015 11(2023), 1 (DE-627)769570496 (DE-600)2735819-7 21693277 nnns volume:11 year:2023 number:1 https://doi.org/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c kostenfrei https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/toc/2169-3277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 |
allfieldsGer |
10.1080/21693277.2023.2179123 doi (DE-627)DOAJ081062206 (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c DE-627 ger DE-627 rakwb eng TS1-2301 HF5001-6182 Menanno Marialuisa verfasserin aut Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor Technology T Manufactures Business Savino Matteo M. verfasserin aut Shafiq Muhammad verfasserin aut In Production and Manufacturing Research: An Open Access Journal Taylor & Francis Group, 2015 11(2023), 1 (DE-627)769570496 (DE-600)2735819-7 21693277 nnns volume:11 year:2023 number:1 https://doi.org/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c kostenfrei https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/toc/2169-3277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 |
allfieldsSound |
10.1080/21693277.2023.2179123 doi (DE-627)DOAJ081062206 (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c DE-627 ger DE-627 rakwb eng TS1-2301 HF5001-6182 Menanno Marialuisa verfasserin aut Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor Technology T Manufactures Business Savino Matteo M. verfasserin aut Shafiq Muhammad verfasserin aut In Production and Manufacturing Research: An Open Access Journal Taylor & Francis Group, 2015 11(2023), 1 (DE-627)769570496 (DE-600)2735819-7 21693277 nnns volume:11 year:2023 number:1 https://doi.org/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c kostenfrei https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 kostenfrei https://doaj.org/toc/2169-3277 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 |
language |
English |
source |
In Production and Manufacturing Research: An Open Access Journal 11(2023), 1 volume:11 year:2023 number:1 |
sourceStr |
In Production and Manufacturing Research: An Open Access Journal 11(2023), 1 volume:11 year:2023 number:1 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor Technology T Manufactures Business |
isfreeaccess_bool |
true |
container_title |
Production and Manufacturing Research: An Open Access Journal |
authorswithroles_txt_mv |
Menanno Marialuisa @@aut@@ Savino Matteo M. @@aut@@ Shafiq Muhammad @@aut@@ |
publishDateDaySort_date |
2023-01-01T00:00:00Z |
hierarchy_top_id |
769570496 |
id |
DOAJ081062206 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ081062206</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414201644.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/21693277.2023.2179123</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ081062206</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TS1-2301</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HF5001-6182</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Menanno Marialuisa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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="520" ind1=" " ind2=" "><subfield code="a">ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Milk-run system (MRS)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IT-based Kanban (ITK)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-lines assembly shop floor</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Manufactures</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Business</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Savino Matteo M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shafiq Muhammad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Production and Manufacturing Research: An Open Access Journal</subfield><subfield code="d">Taylor & Francis Group, 2015</subfield><subfield code="g">11(2023), 1</subfield><subfield code="w">(DE-627)769570496</subfield><subfield code="w">(DE-600)2735819-7</subfield><subfield code="x">21693277</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/21693277.2023.2179123</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3277</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield></datafield></record></collection>
|
callnumber-first |
T - Technology |
author |
Menanno Marialuisa |
spellingShingle |
Menanno Marialuisa misc TS1-2301 misc HF5001-6182 misc Milk-run system (MRS) misc IT-based Kanban (ITK) misc artificial intelligence misc multi-lines assembly shop floor misc Technology misc T misc Manufactures misc Business Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
authorStr |
Menanno Marialuisa |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)769570496 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TS1-2301 |
illustrated |
Not Illustrated |
issn |
21693277 |
topic_title |
TS1-2301 HF5001-6182 Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor Milk-run system (MRS) IT-based Kanban (ITK) artificial intelligence multi-lines assembly shop floor |
topic |
misc TS1-2301 misc HF5001-6182 misc Milk-run system (MRS) misc IT-based Kanban (ITK) misc artificial intelligence misc multi-lines assembly shop floor misc Technology misc T misc Manufactures misc Business |
topic_unstemmed |
misc TS1-2301 misc HF5001-6182 misc Milk-run system (MRS) misc IT-based Kanban (ITK) misc artificial intelligence misc multi-lines assembly shop floor misc Technology misc T misc Manufactures misc Business |
topic_browse |
misc TS1-2301 misc HF5001-6182 misc Milk-run system (MRS) misc IT-based Kanban (ITK) misc artificial intelligence misc multi-lines assembly shop floor misc Technology misc T misc Manufactures misc Business |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Production and Manufacturing Research: An Open Access Journal |
hierarchy_parent_id |
769570496 |
hierarchy_top_title |
Production and Manufacturing Research: An Open Access Journal |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)769570496 (DE-600)2735819-7 |
title |
Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
ctrlnum |
(DE-627)DOAJ081062206 (DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c |
title_full |
Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
author_sort |
Menanno Marialuisa |
journal |
Production and Manufacturing Research: An Open Access Journal |
journalStr |
Production and Manufacturing Research: An Open Access Journal |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
author_browse |
Menanno Marialuisa Savino Matteo M. Shafiq Muhammad |
container_volume |
11 |
class |
TS1-2301 HF5001-6182 |
format_se |
Elektronische Aufsätze |
author-letter |
Menanno Marialuisa |
doi_str_mv |
10.1080/21693277.2023.2179123 |
author2-role |
verfasserin |
title_sort |
optimizing milk-run system and it-based kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
callnumber |
TS1-2301 |
title_auth |
Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
abstract |
ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. |
abstractGer |
ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. |
abstract_unstemmed |
ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor |
url |
https://doi.org/10.1080/21693277.2023.2179123 https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123 https://doaj.org/toc/2169-3277 |
remote_bool |
true |
author2 |
Savino Matteo M. Shafiq Muhammad |
author2Str |
Savino Matteo M. Shafiq Muhammad |
ppnlink |
769570496 |
callnumber-subject |
TS - Manufactures |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1080/21693277.2023.2179123 |
callnumber-a |
TS1-2301 |
up_date |
2024-07-03T18:05:34.521Z |
_version_ |
1803582108622389248 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ081062206</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414201644.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/21693277.2023.2179123</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ081062206</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ05f704c7ec444cf3a9694a99c4e2792c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TS1-2301</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HF5001-6182</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Menanno Marialuisa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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="520" ind1=" " ind2=" "><subfield code="a">ABSTRACTIn this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Milk-run system (MRS)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IT-based Kanban (ITK)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-lines assembly shop floor</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Manufactures</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Business</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Savino Matteo M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shafiq Muhammad</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Production and Manufacturing Research: An Open Access Journal</subfield><subfield code="d">Taylor & Francis Group, 2015</subfield><subfield code="g">11(2023), 1</subfield><subfield code="w">(DE-627)769570496</subfield><subfield code="w">(DE-600)2735819-7</subfield><subfield code="x">21693277</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/21693277.2023.2179123</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/05f704c7ec444cf3a9694a99c4e2792c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/10.1080/21693277.2023.2179123</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3277</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield></datafield></record></collection>
|
score |
7.4015017 |