A new heuristic and an exact approach for a production planning problem
Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assem...
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Gespeichert in:
| Autor*in: |
Auer, Peter [verfasserIn] Dósa, György [verfasserIn] Dulai, Tibor [verfasserIn] Fügenschuh, Armin [verfasserIn] Näser, Peggy [verfasserIn] Ortner, Ronald [verfasserIn] Werner-Stark, Ágnes [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Central European journal of operations research - Heidelberg : Physica-Verl., 1999, 29(2020), 3 vom: 11. Juni, Seite 1079-1113 |
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| Übergeordnetes Werk: |
volume:29 ; year:2020 ; number:3 ; day:11 ; month:06 ; pages:1079-1113 |
| Links: |
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| DOI / URN: |
10.1007/s10100-020-00689-3 |
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| Katalog-ID: |
SPR044419236 |
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| 245 | 1 | 2 | |a A new heuristic and an exact approach for a production planning problem |
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| 520 | |a Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. | ||
| 650 | 4 | |a Production planning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mixed-integer programming |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Heuristics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Simulation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Dósa, György |e verfasserin |4 aut | |
| 700 | 1 | |a Dulai, Tibor |e verfasserin |4 aut | |
| 700 | 1 | |a Fügenschuh, Armin |e verfasserin |4 aut | |
| 700 | 1 | |a Näser, Peggy |e verfasserin |4 aut | |
| 700 | 1 | |a Ortner, Ronald |e verfasserin |4 aut | |
| 700 | 1 | |a Werner-Stark, Ágnes |e verfasserin |4 aut | |
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10.1007/s10100-020-00689-3 doi (DE-627)SPR044419236 (SPR)s10100-020-00689-3-e DE-627 ger DE-627 rakwb eng 300 330 650 ASE 85.03 bkl Auer, Peter verfasserin aut A new heuristic and an exact approach for a production planning problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. Production planning (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Dósa, György verfasserin aut Dulai, Tibor verfasserin aut Fügenschuh, Armin verfasserin aut Näser, Peggy verfasserin aut Ortner, Ronald verfasserin aut Werner-Stark, Ágnes verfasserin aut Enthalten in Central European journal of operations research Heidelberg : Physica-Verl., 1999 29(2020), 3 vom: 11. Juni, Seite 1079-1113 (DE-627)357165160 (DE-600)2093829-9 1613-9178 nnns volume:29 year:2020 number:3 day:11 month:06 pages:1079-1113 https://dx.doi.org/10.1007/s10100-020-00689-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE AR 29 2020 3 11 06 1079-1113 |
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10.1007/s10100-020-00689-3 doi (DE-627)SPR044419236 (SPR)s10100-020-00689-3-e DE-627 ger DE-627 rakwb eng 300 330 650 ASE 85.03 bkl Auer, Peter verfasserin aut A new heuristic and an exact approach for a production planning problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. Production planning (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Dósa, György verfasserin aut Dulai, Tibor verfasserin aut Fügenschuh, Armin verfasserin aut Näser, Peggy verfasserin aut Ortner, Ronald verfasserin aut Werner-Stark, Ágnes verfasserin aut Enthalten in Central European journal of operations research Heidelberg : Physica-Verl., 1999 29(2020), 3 vom: 11. Juni, Seite 1079-1113 (DE-627)357165160 (DE-600)2093829-9 1613-9178 nnns volume:29 year:2020 number:3 day:11 month:06 pages:1079-1113 https://dx.doi.org/10.1007/s10100-020-00689-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE AR 29 2020 3 11 06 1079-1113 |
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10.1007/s10100-020-00689-3 doi (DE-627)SPR044419236 (SPR)s10100-020-00689-3-e DE-627 ger DE-627 rakwb eng 300 330 650 ASE 85.03 bkl Auer, Peter verfasserin aut A new heuristic and an exact approach for a production planning problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. Production planning (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Dósa, György verfasserin aut Dulai, Tibor verfasserin aut Fügenschuh, Armin verfasserin aut Näser, Peggy verfasserin aut Ortner, Ronald verfasserin aut Werner-Stark, Ágnes verfasserin aut Enthalten in Central European journal of operations research Heidelberg : Physica-Verl., 1999 29(2020), 3 vom: 11. Juni, Seite 1079-1113 (DE-627)357165160 (DE-600)2093829-9 1613-9178 nnns volume:29 year:2020 number:3 day:11 month:06 pages:1079-1113 https://dx.doi.org/10.1007/s10100-020-00689-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE AR 29 2020 3 11 06 1079-1113 |
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10.1007/s10100-020-00689-3 doi (DE-627)SPR044419236 (SPR)s10100-020-00689-3-e DE-627 ger DE-627 rakwb eng 300 330 650 ASE 85.03 bkl Auer, Peter verfasserin aut A new heuristic and an exact approach for a production planning problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. Production planning (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Dósa, György verfasserin aut Dulai, Tibor verfasserin aut Fügenschuh, Armin verfasserin aut Näser, Peggy verfasserin aut Ortner, Ronald verfasserin aut Werner-Stark, Ágnes verfasserin aut Enthalten in Central European journal of operations research Heidelberg : Physica-Verl., 1999 29(2020), 3 vom: 11. Juni, Seite 1079-1113 (DE-627)357165160 (DE-600)2093829-9 1613-9178 nnns volume:29 year:2020 number:3 day:11 month:06 pages:1079-1113 https://dx.doi.org/10.1007/s10100-020-00689-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE AR 29 2020 3 11 06 1079-1113 |
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10.1007/s10100-020-00689-3 doi (DE-627)SPR044419236 (SPR)s10100-020-00689-3-e DE-627 ger DE-627 rakwb eng 300 330 650 ASE 85.03 bkl Auer, Peter verfasserin aut A new heuristic and an exact approach for a production planning problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. Production planning (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 Dósa, György verfasserin aut Dulai, Tibor verfasserin aut Fügenschuh, Armin verfasserin aut Näser, Peggy verfasserin aut Ortner, Ronald verfasserin aut Werner-Stark, Ágnes verfasserin aut Enthalten in Central European journal of operations research Heidelberg : Physica-Verl., 1999 29(2020), 3 vom: 11. Juni, Seite 1079-1113 (DE-627)357165160 (DE-600)2093829-9 1613-9178 nnns volume:29 year:2020 number:3 day:11 month:06 pages:1079-1113 https://dx.doi.org/10.1007/s10100-020-00689-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 ASE AR 29 2020 3 11 06 1079-1113 |
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Auer, Peter @@aut@@ Dósa, György @@aut@@ Dulai, Tibor @@aut@@ Fügenschuh, Armin @@aut@@ Näser, Peggy @@aut@@ Ortner, Ronald @@aut@@ Werner-Stark, Ágnes @@aut@@ |
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Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Production planning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mixed-integer programming</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heuristics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dósa, György</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dulai, Tibor</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fügenschuh, Armin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Näser, Peggy</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ortner, Ronald</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Werner-Stark, Ágnes</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Central European journal of operations research</subfield><subfield code="d">Heidelberg : Physica-Verl., 1999</subfield><subfield code="g">29(2020), 3 vom: 11. 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Auer, Peter |
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Auer, Peter ddc 300 bkl 85.03 misc Production planning misc Mixed-integer programming misc Heuristics misc Simulation A new heuristic and an exact approach for a production planning problem |
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300 330 650 ASE 85.03 bkl A new heuristic and an exact approach for a production planning problem Production planning (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Simulation (dpeaa)DE-He213 |
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A new heuristic and an exact approach for a production planning problem |
| abstract |
Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. © The Author(s) 2020 |
| abstractGer |
Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. © The Author(s) 2020 |
| abstract_unstemmed |
Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time. © The Author(s) 2020 |
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3 |
| title_short |
A new heuristic and an exact approach for a production planning problem |
| url |
https://dx.doi.org/10.1007/s10100-020-00689-3 |
| remote_bool |
true |
| author2 |
Dósa, György Dulai, Tibor Fügenschuh, Armin Näser, Peggy Ortner, Ronald Werner-Stark, Ágnes |
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Dósa, György Dulai, Tibor Fügenschuh, Armin Näser, Peggy Ortner, Ronald Werner-Stark, Ágnes |
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| doi_str |
10.1007/s10100-020-00689-3 |
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2024-07-04T00:35:04.308Z |
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| score |
7.401288 |