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...
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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:	Volltext Volltext Link aufrufen Terms of use

DOI / URN:	10.3390/joitmc5020026

Katalog-ID:	1669512703

Internformat


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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
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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
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title	Adaptive large neighborhood search for a production planning problem arising in pig farming
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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
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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.
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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/
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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
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doi_str	10.3390/joitmc5020026
callnumber-a	--%%--
up_date	2024-07-05T00:27:05.515Z
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Adaptive large neighborhood search for a production planning problem arising in pig farming

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