Adaptive large neighborhood search for a production planning problem arising in pig farming
This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of t...
Ausführliche Beschreibung
Autor*in: |
Nat Praseeratasang [verfasserIn] Rapeepan Pitakaso [verfasserIn] Kanchana Sethanan [verfasserIn] Sasitorn Kaewman [verfasserIn] Golinska-Dawson, Paulina [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2019 |
---|
Übergeordnetes Werk: |
Enthalten in: Journal of open innovation - Basel : MDPI, 2015, 5(2019), 2/26 vom: Juni, Seite 1-21 |
---|---|
Übergeordnetes Werk: |
volume:5 ; year:2019 ; number:2/26 ; month:06 ; pages:1-21 |
Links: |
---|
DOI / URN: |
10.3390/joitmc5020026 |
---|
Katalog-ID: |
1669512703 |
---|
LEADER | 01000caa a2200265 4500 | ||
---|---|---|---|
001 | 1669512703 | ||
003 | DE-627 | ||
005 | 20210924151702.0 | ||
007 | cr uuu---uuuuu | ||
008 | 190719s2019 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/joitmc5020026 |2 doi | |
024 | 7 | |a 10419/241317 |2 hdl | |
035 | |a (DE-627)1669512703 | ||
035 | |a (DE-599)KXP1669512703 | ||
040 | |a DE-627 |b ger |c DE-627 |e rda | ||
041 | |a eng | ||
100 | 0 | |a Nat Praseeratasang |e verfasserin |0 (DE-588)119202219X |0 (DE-627)1670492893 |4 aut | |
245 | 1 | 0 | |a Adaptive large neighborhood search for a production planning problem arising in pig farming |c Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson |
264 | 1 | |c 2019 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. | ||
700 | 0 | |a Rapeepan Pitakaso |e verfasserin |0 (DE-588)1192022262 |0 (DE-627)1670492974 |4 aut | |
700 | 0 | |a Kanchana Sethanan |e verfasserin |0 (DE-588)1192022386 |0 (DE-627)1670493075 |0 (DE-576)442661126 |4 aut | |
700 | 0 | |a Sasitorn Kaewman |e verfasserin |0 (DE-588)1192022580 |0 (DE-627)1670493202 |4 aut | |
700 | 1 | |a Golinska-Dawson, Paulina |e verfasserin |0 (DE-588)1074418050 |0 (DE-627)832173304 |0 (DE-576)442661533 |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Journal of open innovation |d Basel : MDPI, 2015 |g 5(2019), 2/26 vom: Juni, Seite 1-21 |h Online-Ressource |w (DE-627)833526413 |w (DE-600)2832108-X |w (DE-576)444393463 |x 2199-8531 |7 nnns |
773 | 1 | 8 | |g volume:5 |g year:2019 |g number:2/26 |g month:06 |g pages:1-21 |
856 | 4 | 0 | |u https://doi.org/10.3390/joitmc5020026 |x Resolving-System |z kostenfrei |3 Volltext |
856 | 4 | 0 | |u https://www.mdpi.com/2199-8531/5/2/26/pdf |x Verlag |z kostenfrei |3 Volltext |
856 | 4 | 0 | |u http://hdl.handle.net/10419/241317 |x Resolving-System |z kostenfrei |
856 | 4 | 2 | |u http://creativecommons.org/licenses/by/4.0/ |x Verlag |y Terms of use |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ILN_26 | ||
912 | |a ISIL_DE-206 | ||
912 | |a SYSFLAG_1 | ||
912 | |a GBV_KXP | ||
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_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_224 | ||
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_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2007 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2026 | ||
912 | |a GBV_ILN_2027 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2049 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2088 | ||
912 | |a GBV_ILN_2106 | ||
912 | |a GBV_ILN_2110 | ||
912 | |a GBV_ILN_2112 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2129 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
912 | |a GBV_ILN_2470 | ||
912 | |a GBV_ILN_2507 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4035 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4242 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4251 | ||
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_4326 | ||
912 | |a GBV_ILN_4333 | ||
912 | |a GBV_ILN_4334 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4393 | ||
912 | |a GBV_ILN_4700 | ||
912 | |a GBV_ILN_2403 | ||
912 | |a GBV_ILN_2403 | ||
912 | |a ISIL_DE-LFER | ||
951 | |a AR | ||
952 | |d 5 |j 2019 |e 2/26 |c 6 |h 1-21 | ||
980 | |2 26 |1 01 |x 0206 |b 3495168389 |y x1k |z 19-07-19 | ||
980 | |2 2403 |1 01 |x DE-LFER |b 3596189225 |c 00 |f --%%-- |d --%%-- |e n |j --%%-- |y l01 |z 17-02-20 | ||
981 | |2 2403 |1 01 |x DE-LFER |r https://doi.org/10.3390/joitmc5020026 | ||
981 | |2 2403 |1 01 |x DE-LFER |r https://www.mdpi.com/2199-8531/5/2/26/pdf | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a ant colony optimization | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a adaptive large neighborhood search | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a assignment problems | ||
982 | |2 26 |1 00 |x DE-206 |8 56 |a scheduling problems |
author_variant |
n p np r p rp k s ks s k sk p g d pgd |
---|---|
matchkey_str |
article:21998531:2019----::dpieagnihohosacfrpoutopannpo |
hierarchy_sort_str |
2019 |
publishDate |
2019 |
allfields |
10.3390/joitmc5020026 doi 10419/241317 hdl (DE-627)1669512703 (DE-599)KXP1669512703 DE-627 ger DE-627 rda eng Nat Praseeratasang verfasserin (DE-588)119202219X (DE-627)1670492893 aut Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. Rapeepan Pitakaso verfasserin (DE-588)1192022262 (DE-627)1670492974 aut Kanchana Sethanan verfasserin (DE-588)1192022386 (DE-627)1670493075 (DE-576)442661126 aut Sasitorn Kaewman verfasserin (DE-588)1192022580 (DE-627)1670493202 aut Golinska-Dawson, Paulina verfasserin (DE-588)1074418050 (DE-627)832173304 (DE-576)442661533 aut Enthalten in Journal of open innovation Basel : MDPI, 2015 5(2019), 2/26 vom: Juni, Seite 1-21 Online-Ressource (DE-627)833526413 (DE-600)2832108-X (DE-576)444393463 2199-8531 nnns volume:5 year:2019 number:2/26 month:06 pages:1-21 https://doi.org/10.3390/joitmc5020026 Resolving-System kostenfrei Volltext https://www.mdpi.com/2199-8531/5/2/26/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/241317 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 5 2019 2/26 6 1-21 26 01 0206 3495168389 x1k 19-07-19 2403 01 DE-LFER 3596189225 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/joitmc5020026 2403 01 DE-LFER https://www.mdpi.com/2199-8531/5/2/26/pdf 26 00 DE-206 56 ant colony optimization 26 00 DE-206 56 adaptive large neighborhood search 26 00 DE-206 56 assignment problems 26 00 DE-206 56 scheduling problems |
spelling |
10.3390/joitmc5020026 doi 10419/241317 hdl (DE-627)1669512703 (DE-599)KXP1669512703 DE-627 ger DE-627 rda eng Nat Praseeratasang verfasserin (DE-588)119202219X (DE-627)1670492893 aut Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. Rapeepan Pitakaso verfasserin (DE-588)1192022262 (DE-627)1670492974 aut Kanchana Sethanan verfasserin (DE-588)1192022386 (DE-627)1670493075 (DE-576)442661126 aut Sasitorn Kaewman verfasserin (DE-588)1192022580 (DE-627)1670493202 aut Golinska-Dawson, Paulina verfasserin (DE-588)1074418050 (DE-627)832173304 (DE-576)442661533 aut Enthalten in Journal of open innovation Basel : MDPI, 2015 5(2019), 2/26 vom: Juni, Seite 1-21 Online-Ressource (DE-627)833526413 (DE-600)2832108-X (DE-576)444393463 2199-8531 nnns volume:5 year:2019 number:2/26 month:06 pages:1-21 https://doi.org/10.3390/joitmc5020026 Resolving-System kostenfrei Volltext https://www.mdpi.com/2199-8531/5/2/26/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/241317 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 5 2019 2/26 6 1-21 26 01 0206 3495168389 x1k 19-07-19 2403 01 DE-LFER 3596189225 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/joitmc5020026 2403 01 DE-LFER https://www.mdpi.com/2199-8531/5/2/26/pdf 26 00 DE-206 56 ant colony optimization 26 00 DE-206 56 adaptive large neighborhood search 26 00 DE-206 56 assignment problems 26 00 DE-206 56 scheduling problems |
allfields_unstemmed |
10.3390/joitmc5020026 doi 10419/241317 hdl (DE-627)1669512703 (DE-599)KXP1669512703 DE-627 ger DE-627 rda eng Nat Praseeratasang verfasserin (DE-588)119202219X (DE-627)1670492893 aut Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. Rapeepan Pitakaso verfasserin (DE-588)1192022262 (DE-627)1670492974 aut Kanchana Sethanan verfasserin (DE-588)1192022386 (DE-627)1670493075 (DE-576)442661126 aut Sasitorn Kaewman verfasserin (DE-588)1192022580 (DE-627)1670493202 aut Golinska-Dawson, Paulina verfasserin (DE-588)1074418050 (DE-627)832173304 (DE-576)442661533 aut Enthalten in Journal of open innovation Basel : MDPI, 2015 5(2019), 2/26 vom: Juni, Seite 1-21 Online-Ressource (DE-627)833526413 (DE-600)2832108-X (DE-576)444393463 2199-8531 nnns volume:5 year:2019 number:2/26 month:06 pages:1-21 https://doi.org/10.3390/joitmc5020026 Resolving-System kostenfrei Volltext https://www.mdpi.com/2199-8531/5/2/26/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/241317 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 5 2019 2/26 6 1-21 26 01 0206 3495168389 x1k 19-07-19 2403 01 DE-LFER 3596189225 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/joitmc5020026 2403 01 DE-LFER https://www.mdpi.com/2199-8531/5/2/26/pdf 26 00 DE-206 56 ant colony optimization 26 00 DE-206 56 adaptive large neighborhood search 26 00 DE-206 56 assignment problems 26 00 DE-206 56 scheduling problems |
allfieldsGer |
10.3390/joitmc5020026 doi 10419/241317 hdl (DE-627)1669512703 (DE-599)KXP1669512703 DE-627 ger DE-627 rda eng Nat Praseeratasang verfasserin (DE-588)119202219X (DE-627)1670492893 aut Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. Rapeepan Pitakaso verfasserin (DE-588)1192022262 (DE-627)1670492974 aut Kanchana Sethanan verfasserin (DE-588)1192022386 (DE-627)1670493075 (DE-576)442661126 aut Sasitorn Kaewman verfasserin (DE-588)1192022580 (DE-627)1670493202 aut Golinska-Dawson, Paulina verfasserin (DE-588)1074418050 (DE-627)832173304 (DE-576)442661533 aut Enthalten in Journal of open innovation Basel : MDPI, 2015 5(2019), 2/26 vom: Juni, Seite 1-21 Online-Ressource (DE-627)833526413 (DE-600)2832108-X (DE-576)444393463 2199-8531 nnns volume:5 year:2019 number:2/26 month:06 pages:1-21 https://doi.org/10.3390/joitmc5020026 Resolving-System kostenfrei Volltext https://www.mdpi.com/2199-8531/5/2/26/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/241317 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 5 2019 2/26 6 1-21 26 01 0206 3495168389 x1k 19-07-19 2403 01 DE-LFER 3596189225 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/joitmc5020026 2403 01 DE-LFER https://www.mdpi.com/2199-8531/5/2/26/pdf 26 00 DE-206 56 ant colony optimization 26 00 DE-206 56 adaptive large neighborhood search 26 00 DE-206 56 assignment problems 26 00 DE-206 56 scheduling problems |
allfieldsSound |
10.3390/joitmc5020026 doi 10419/241317 hdl (DE-627)1669512703 (DE-599)KXP1669512703 DE-627 ger DE-627 rda eng Nat Praseeratasang verfasserin (DE-588)119202219X (DE-627)1670492893 aut Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. Rapeepan Pitakaso verfasserin (DE-588)1192022262 (DE-627)1670492974 aut Kanchana Sethanan verfasserin (DE-588)1192022386 (DE-627)1670493075 (DE-576)442661126 aut Sasitorn Kaewman verfasserin (DE-588)1192022580 (DE-627)1670493202 aut Golinska-Dawson, Paulina verfasserin (DE-588)1074418050 (DE-627)832173304 (DE-576)442661533 aut Enthalten in Journal of open innovation Basel : MDPI, 2015 5(2019), 2/26 vom: Juni, Seite 1-21 Online-Ressource (DE-627)833526413 (DE-600)2832108-X (DE-576)444393463 2199-8531 nnns volume:5 year:2019 number:2/26 month:06 pages:1-21 https://doi.org/10.3390/joitmc5020026 Resolving-System kostenfrei Volltext https://www.mdpi.com/2199-8531/5/2/26/pdf Verlag kostenfrei Volltext http://hdl.handle.net/10419/241317 Resolving-System kostenfrei http://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 5 2019 2/26 6 1-21 26 01 0206 3495168389 x1k 19-07-19 2403 01 DE-LFER 3596189225 00 --%%-- --%%-- n --%%-- l01 17-02-20 2403 01 DE-LFER https://doi.org/10.3390/joitmc5020026 2403 01 DE-LFER https://www.mdpi.com/2199-8531/5/2/26/pdf 26 00 DE-206 56 ant colony optimization 26 00 DE-206 56 adaptive large neighborhood search 26 00 DE-206 56 assignment problems 26 00 DE-206 56 scheduling problems |
language |
English |
source |
Enthalten in Journal of open innovation 5(2019), 2/26 vom: Juni, Seite 1-21 volume:5 year:2019 number:2/26 month:06 pages:1-21 |
sourceStr |
Enthalten in Journal of open innovation 5(2019), 2/26 vom: Juni, Seite 1-21 volume:5 year:2019 number:2/26 month:06 pages:1-21 |
format_phy_str_mv |
Article |
building |
26:1 2403:0 |
institution |
findex.gbv.de |
selectbib_iln_str_mv |
26@1k 2403@01 |
sw_local_iln_str_mv |
26:ant colony optimization DE-206:ant colony optimization 26:adaptive large neighborhood search DE-206:adaptive large neighborhood search 26:assignment problems DE-206:assignment problems 26:scheduling problems DE-206:scheduling problems |
isfreeaccess_bool |
true |
container_title |
Journal of open innovation |
authorswithroles_txt_mv |
Nat Praseeratasang @@aut@@ Rapeepan Pitakaso @@aut@@ Kanchana Sethanan @@aut@@ Sasitorn Kaewman @@aut@@ Golinska-Dawson, Paulina @@aut@@ |
publishDateDaySort_date |
2019-06-01T00:00:00Z |
hierarchy_top_id |
833526413 |
id |
1669512703 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1669512703</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210924151702.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190719s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/joitmc5020026</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/241317</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1669512703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1669512703</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nat Praseeratasang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)119202219X</subfield><subfield code="0">(DE-627)1670492893</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive large neighborhood search for a production planning problem arising in pig farming</subfield><subfield code="c">Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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">This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rapeepan Pitakaso</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1192022262</subfield><subfield code="0">(DE-627)1670492974</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kanchana Sethanan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1192022386</subfield><subfield code="0">(DE-627)1670493075</subfield><subfield code="0">(DE-576)442661126</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sasitorn Kaewman</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1192022580</subfield><subfield code="0">(DE-627)1670493202</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Golinska-Dawson, Paulina</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1074418050</subfield><subfield code="0">(DE-627)832173304</subfield><subfield code="0">(DE-576)442661533</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of open innovation</subfield><subfield code="d">Basel : MDPI, 2015</subfield><subfield code="g">5(2019), 2/26 vom: Juni, Seite 1-21</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)833526413</subfield><subfield code="w">(DE-600)2832108-X</subfield><subfield code="w">(DE-576)444393463</subfield><subfield code="x">2199-8531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2/26</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:1-21</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/joitmc5020026</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2199-8531/5/2/26/pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdl.handle.net/10419/241317</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://creativecommons.org/licenses/by/4.0/</subfield><subfield code="x">Verlag</subfield><subfield code="y">Terms of use</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</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_206</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_224</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_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</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_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</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_4035</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_4242</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_4251</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_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</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_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2019</subfield><subfield code="e">2/26</subfield><subfield code="c">6</subfield><subfield code="h">1-21</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">3495168389</subfield><subfield code="y">x1k</subfield><subfield code="z">19-07-19</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">3596189225</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">17-02-20</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.3390/joitmc5020026</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://www.mdpi.com/2199-8531/5/2/26/pdf</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">ant colony optimization</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">adaptive large neighborhood search</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">assignment problems</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">scheduling problems</subfield></datafield></record></collection>
|
standort_str_mv |
--%%-- |
standort_iln_str_mv |
2403:--%%-- DE-LFER:--%%-- |
author |
Nat Praseeratasang |
spellingShingle |
Nat Praseeratasang 26 ant colony optimization 26 adaptive large neighborhood search 26 assignment problems 26 scheduling problems Adaptive large neighborhood search for a production planning problem arising in pig farming |
authorStr |
Nat Praseeratasang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)833526413 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut |
typewithnormlink_str_mv |
DifferentiatedPerson@(DE-588)119202219X Person@(DE-588)119202219X Person@(DE-588)1192022262 DifferentiatedPerson@(DE-588)1192022262 DifferentiatedPerson@(DE-588)1192022386 Person@(DE-588)1192022386 Person@(DE-588)1192022580 DifferentiatedPerson@(DE-588)1192022580 DifferentiatedPerson@(DE-588)1074418050 Person@(DE-588)1074418050 |
collection |
KXP GVK SWB |
remote_str |
true |
last_changed_iln_str_mv |
26@19-07-19 2403@17-02-20 |
illustrated |
Not Illustrated |
issn |
2199-8531 |
topic_title |
26 00 DE-206 56 ant colony optimization 26 00 DE-206 56 adaptive large neighborhood search 26 00 DE-206 56 assignment problems 26 00 DE-206 56 scheduling problems Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson |
topic |
26 ant colony optimization 26 adaptive large neighborhood search 26 assignment problems 26 scheduling problems |
topic_unstemmed |
26 ant colony optimization 26 adaptive large neighborhood search 26 assignment problems 26 scheduling problems |
topic_browse |
26 ant colony optimization 26 adaptive large neighborhood search 26 assignment problems 26 scheduling problems |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
standort_txtP_mv |
--%%-- |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Journal of open innovation |
normlinkwithtype_str_mv |
(DE-588)119202219X@DifferentiatedPerson (DE-588)119202219X@Person (DE-588)1192022262@Person (DE-588)1192022262@DifferentiatedPerson (DE-588)1192022386@DifferentiatedPerson (DE-588)1192022386@Person (DE-588)1192022580@Person (DE-588)1192022580@DifferentiatedPerson (DE-588)1074418050@DifferentiatedPerson (DE-588)1074418050@Person |
hierarchy_parent_id |
833526413 |
signature |
--%%-- |
signature_str_mv |
--%%-- |
hierarchy_top_title |
Journal of open innovation |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)833526413 (DE-600)2832108-X (DE-576)444393463 |
normlinkwithrole_str_mv |
(DE-588)119202219X@@aut@@ (DE-588)1192022262@@aut@@ (DE-588)1192022386@@aut@@ (DE-588)1192022580@@aut@@ (DE-588)1074418050@@aut@@ |
title |
Adaptive large neighborhood search for a production planning problem arising in pig farming |
ctrlnum |
(DE-627)1669512703 (DE-599)KXP1669512703 |
title_full |
Adaptive large neighborhood search for a production planning problem arising in pig farming Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson |
author_sort |
Nat Praseeratasang |
journal |
Journal of open innovation |
journalStr |
Journal of open innovation |
callnumber-first-code |
- |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2019 |
contenttype_str_mv |
txt |
container_start_page |
1 |
author_browse |
Nat Praseeratasang Rapeepan Pitakaso Kanchana Sethanan Sasitorn Kaewman Golinska-Dawson, Paulina |
selectkey |
26:x 2403:l |
container_volume |
5 |
format_se |
Elektronische Aufsätze |
author-letter |
Nat Praseeratasang |
doi_str_mv |
10.3390/joitmc5020026 |
normlink |
119202219X 1670492893 1192022262 1670492974 1192022386 1670493075 442661126 1192022580 1670493202 1074418050 832173304 442661533 |
normlink_prefix_str_mv |
(DE-588)119202219X (DE-627)1670492893 (DE-588)1192022262 (DE-627)1670492974 (DE-588)1192022386 (DE-627)1670493075 (DE-576)442661126 (DE-588)1192022580 (DE-627)1670493202 (DE-588)1074418050 (DE-627)832173304 (DE-576)442661533 |
author2-role |
verfasserin |
title_sort |
adaptive large neighborhood search for a production planning problem arising in pig farming |
title_auth |
Adaptive large neighborhood search for a production planning problem arising in pig farming |
abstract |
This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. |
abstractGer |
This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. |
abstract_unstemmed |
This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS. |
collection_details |
GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 ISIL_DE-LFER |
container_issue |
2/26 |
title_short |
Adaptive large neighborhood search for a production planning problem arising in pig farming |
url |
https://doi.org/10.3390/joitmc5020026 https://www.mdpi.com/2199-8531/5/2/26/pdf http://hdl.handle.net/10419/241317 http://creativecommons.org/licenses/by/4.0/ |
ausleihindikator_str_mv |
26 2403:n |
rolewithnormlink_str_mv |
@@aut@@(DE-588)119202219X @@aut@@(DE-588)1192022262 @@aut@@(DE-588)1192022386 @@aut@@(DE-588)1192022580 @@aut@@(DE-588)1074418050 |
remote_bool |
true |
author2 |
Rapeepan Pitakaso Kanchana Sethanan Sasitorn Kaewman Golinska-Dawson, Paulina |
author2Str |
Rapeepan Pitakaso Kanchana Sethanan Sasitorn Kaewman Golinska-Dawson, Paulina |
ppnlink |
833526413 |
GND_str_mv |
Praseeratasang, Nat Nat Praseeratasang Pitakaso, Rapeepan Rapeepan Pitakaso Sethanan, Kanchana Kanchana Sethanan Kaewman, Sasitorn Sasitorn Kaewman Golinska, Paulina Dawson, Paulina Golinska- Golinska-Dawson, Paulina |
GND_txt_mv |
Praseeratasang, Nat Nat Praseeratasang Pitakaso, Rapeepan Rapeepan Pitakaso Sethanan, Kanchana Kanchana Sethanan Kaewman, Sasitorn Sasitorn Kaewman Golinska, Paulina Dawson, Paulina Golinska- Golinska-Dawson, Paulina |
GND_txtF_mv |
Praseeratasang, Nat Nat Praseeratasang Pitakaso, Rapeepan Rapeepan Pitakaso Sethanan, Kanchana Kanchana Sethanan Kaewman, Sasitorn Sasitorn Kaewman Golinska, Paulina Dawson, Paulina Golinska- Golinska-Dawson, Paulina |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/joitmc5020026 |
callnumber-a |
--%%-- |
up_date |
2024-07-05T00:27:05.515Z |
_version_ |
1803696708535713793 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1669512703</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210924151702.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190719s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/joitmc5020026</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/241317</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1669512703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1669512703</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Nat Praseeratasang</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)119202219X</subfield><subfield code="0">(DE-627)1670492893</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive large neighborhood search for a production planning problem arising in pig farming</subfield><subfield code="c">Nat Praseeratasang, Rapeepan Pitakaso, Kanchana Sethanan, Sasitorn Kaewman and Paulina Golinska-Dawson</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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">This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480-620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rapeepan Pitakaso</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1192022262</subfield><subfield code="0">(DE-627)1670492974</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kanchana Sethanan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1192022386</subfield><subfield code="0">(DE-627)1670493075</subfield><subfield code="0">(DE-576)442661126</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sasitorn Kaewman</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1192022580</subfield><subfield code="0">(DE-627)1670493202</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Golinska-Dawson, Paulina</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1074418050</subfield><subfield code="0">(DE-627)832173304</subfield><subfield code="0">(DE-576)442661533</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of open innovation</subfield><subfield code="d">Basel : MDPI, 2015</subfield><subfield code="g">5(2019), 2/26 vom: Juni, Seite 1-21</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)833526413</subfield><subfield code="w">(DE-600)2832108-X</subfield><subfield code="w">(DE-576)444393463</subfield><subfield code="x">2199-8531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2/26</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:1-21</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/joitmc5020026</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2199-8531/5/2/26/pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://hdl.handle.net/10419/241317</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://creativecommons.org/licenses/by/4.0/</subfield><subfield code="x">Verlag</subfield><subfield code="y">Terms of use</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</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_206</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_224</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_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</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_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</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_4035</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_4242</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_4251</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_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</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_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2019</subfield><subfield code="e">2/26</subfield><subfield code="c">6</subfield><subfield code="h">1-21</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">3495168389</subfield><subfield code="y">x1k</subfield><subfield code="z">19-07-19</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">3596189225</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">17-02-20</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.3390/joitmc5020026</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://www.mdpi.com/2199-8531/5/2/26/pdf</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">ant colony optimization</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">adaptive large neighborhood search</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">assignment problems</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="8">56</subfield><subfield code="a">scheduling problems</subfield></datafield></record></collection>
|
score |
7.4006147 |