A mathematical model for the scheduling and definition of mining cuts in short-term mine planning
Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term productio...
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Gespeichert in:
| Autor*in: |
Nelis, Gonzalo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Optimization and engineering - Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000, 23(2021), 1 vom: 03. Jan., Seite 233-257 |
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| Übergeordnetes Werk: |
volume:23 ; year:2021 ; number:1 ; day:03 ; month:01 ; pages:233-257 |
| Links: |
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| DOI / URN: |
10.1007/s11081-020-09580-1 |
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| Katalog-ID: |
SPR046720855 |
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| 100 | 1 | |a Nelis, Gonzalo |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A mathematical model for the scheduling and definition of mining cuts in short-term mine planning |
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| 520 | |a Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. | ||
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| 650 | 4 | |a Mixed-integer programming |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Morales, Nelson |4 aut | |
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10.1007/s11081-020-09580-1 doi (DE-627)SPR046720855 (SPR)s11081-020-09580-1-e DE-627 ger DE-627 rakwb eng Nelis, Gonzalo verfasserin aut A mathematical model for the scheduling and definition of mining cuts in short-term mine planning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. Short-term mine planning (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Mining cuts (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Morales, Nelson aut Enthalten in Optimization and engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 23(2021), 1 vom: 03. Jan., Seite 233-257 (DE-627)320588300 (DE-600)2018576-5 1573-2924 nnns volume:23 year:2021 number:1 day:03 month:01 pages:233-257 https://dx.doi.org/10.1007/s11081-020-09580-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_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 AR 23 2021 1 03 01 233-257 |
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10.1007/s11081-020-09580-1 doi (DE-627)SPR046720855 (SPR)s11081-020-09580-1-e DE-627 ger DE-627 rakwb eng Nelis, Gonzalo verfasserin aut A mathematical model for the scheduling and definition of mining cuts in short-term mine planning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. Short-term mine planning (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Mining cuts (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Morales, Nelson aut Enthalten in Optimization and engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 23(2021), 1 vom: 03. Jan., Seite 233-257 (DE-627)320588300 (DE-600)2018576-5 1573-2924 nnns volume:23 year:2021 number:1 day:03 month:01 pages:233-257 https://dx.doi.org/10.1007/s11081-020-09580-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_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 AR 23 2021 1 03 01 233-257 |
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10.1007/s11081-020-09580-1 doi (DE-627)SPR046720855 (SPR)s11081-020-09580-1-e DE-627 ger DE-627 rakwb eng Nelis, Gonzalo verfasserin aut A mathematical model for the scheduling and definition of mining cuts in short-term mine planning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. Short-term mine planning (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Mining cuts (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Morales, Nelson aut Enthalten in Optimization and engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 23(2021), 1 vom: 03. Jan., Seite 233-257 (DE-627)320588300 (DE-600)2018576-5 1573-2924 nnns volume:23 year:2021 number:1 day:03 month:01 pages:233-257 https://dx.doi.org/10.1007/s11081-020-09580-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_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 AR 23 2021 1 03 01 233-257 |
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10.1007/s11081-020-09580-1 doi (DE-627)SPR046720855 (SPR)s11081-020-09580-1-e DE-627 ger DE-627 rakwb eng Nelis, Gonzalo verfasserin aut A mathematical model for the scheduling and definition of mining cuts in short-term mine planning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. Short-term mine planning (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Mining cuts (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Morales, Nelson aut Enthalten in Optimization and engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 23(2021), 1 vom: 03. Jan., Seite 233-257 (DE-627)320588300 (DE-600)2018576-5 1573-2924 nnns volume:23 year:2021 number:1 day:03 month:01 pages:233-257 https://dx.doi.org/10.1007/s11081-020-09580-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_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 AR 23 2021 1 03 01 233-257 |
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10.1007/s11081-020-09580-1 doi (DE-627)SPR046720855 (SPR)s11081-020-09580-1-e DE-627 ger DE-627 rakwb eng Nelis, Gonzalo verfasserin aut A mathematical model for the scheduling and definition of mining cuts in short-term mine planning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. Short-term mine planning (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Mining cuts (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 Morales, Nelson aut Enthalten in Optimization and engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 23(2021), 1 vom: 03. Jan., Seite 233-257 (DE-627)320588300 (DE-600)2018576-5 1573-2924 nnns volume:23 year:2021 number:1 day:03 month:01 pages:233-257 https://dx.doi.org/10.1007/s11081-020-09580-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_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 AR 23 2021 1 03 01 233-257 |
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This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. 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A mathematical model for the scheduling and definition of mining cuts in short-term mine planning Short-term mine planning (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Mining cuts (dpeaa)DE-He213 Mixed-integer programming (dpeaa)DE-He213 |
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mathematical model for the scheduling and definition of mining cuts in short-term mine planning |
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A mathematical model for the scheduling and definition of mining cuts in short-term mine planning |
| abstract |
Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. © Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. © Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization tools, and therefore, there is a risk of generating suboptimal results. Several approaches have been proposed in the literature to deal with these issues, either generating operational mining cuts or obtaining a mining schedule to fulfill the short-term constraints. However, an integrated approach has remained an open challenge. In this paper, we propose an optimization model to tackle the operational and scheduling issues simultaneously. The model defines the mining cut configuration and the production schedule in the short-term. It is based on representative Selective Mining Units (SMUs) as the potential locations of the mining cuts and then each SMU is assigned to one of these locations. We tested the model with a real case study, and it was able to generate mining cuts and an extraction sequence fulfilling mining, processing and operational constraints, as well as access restrictions given by the ramp location in each bench. The mining cut design captured most of the profit, and thus it can be used as a guide for the short-term mine planner. The location of the representative SMUs and the precedence definition both impact the mining cut configuration, and future research could address how to incorporate different operational considerations and strategies on the location of these representatives. © Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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A mathematical model for the scheduling and definition of mining cuts in short-term mine planning |
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Morales, Nelson |
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