Optimization Production Scheduling of Underground Backfilling Mining Based on NSGA-II
In the green backfilling mining of underground coal mines, gangue in a coal seam is used to replace and fill goafs. However, the gas drainage time changes in the amount of gangue output and order in the extraction sequence of stope blocks, thereby changing the production output. A multi-objective in...
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| Vydané v: | Mining, metallurgy & exploration Ročník 39; číslo 4; s. 1521 - 1536 |
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| Jazyk: | English |
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Springer International Publishing
01.08.2022
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| ISSN: | 2524-3462, 2524-3470 |
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| Abstract | In the green backfilling mining of underground coal mines, gangue in a coal seam is used to replace and fill goafs. However, the gas drainage time changes in the amount of gangue output and order in the extraction sequence of stope blocks, thereby changing the production output. A multi-objective integer programming model was proposed to solve this based on an improved non-dominated sorting genetic algorithm. The results show a feasible gangue filling rate can be found and the extraction sequence for a long-term planning time is optimized. It increases the planned annual output by 26% and reduces equipment idle time.
Highlights
Solving the change of production scheduling caused by "under three", gas drainage, etc.
Establishing a method to find suitable gangue filling rate in backfilling mining.
Improving the mixed population coding in the NGSA-II for solving production scheduling.
Adding an upper layer with greedy strategy to control the Pareto equilibrium frontier.
Establishing a constraint to limit deadlocks in production scheduling solutions. |
|---|---|
| AbstractList | In the green backfilling mining of underground coal mines, gangue in a coal seam is used to replace and fill goafs. However, the gas drainage time changes in the amount of gangue output and order in the extraction sequence of stope blocks, thereby changing the production output. A multi-objective integer programming model was proposed to solve this based on an improved non-dominated sorting genetic algorithm. The results show a feasible gangue filling rate can be found and the extraction sequence for a long-term planning time is optimized. It increases the planned annual output by 26% and reduces equipment idle time.
Highlights
Solving the change of production scheduling caused by "under three", gas drainage, etc.
Establishing a method to find suitable gangue filling rate in backfilling mining.
Improving the mixed population coding in the NGSA-II for solving production scheduling.
Adding an upper layer with greedy strategy to control the Pareto equilibrium frontier.
Establishing a constraint to limit deadlocks in production scheduling solutions. |
| Author | Wang, Yutao Bao, Yu Zhang, Aijuan Zhao, Liang |
| Author_xml | – sequence: 1 givenname: Yu orcidid: 0000-0002-5276-7532 surname: Bao fullname: Bao, Yu email: baoyu@cumt.edu.cn organization: Department of Computer Science and Technology, China University of Mining and Technology, Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, China University of Mining and Technology – sequence: 2 givenname: Yutao surname: Wang fullname: Wang, Yutao organization: Department of Computer Science and Technology, China University of Mining and Technology – sequence: 3 givenname: Liang surname: Zhao fullname: Zhao, Liang organization: School of Mine, China University of Mining and Technology – sequence: 4 givenname: Aijuan surname: Zhang fullname: Zhang, Aijuan organization: Department of Computer Science and Technology, China University of Mining and Technology, Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, China University of Mining and Technology |
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| Cites_doi | 10.3390/en10091309 10.1287/inte.2013.0730 10.1016/j.ejor.2016.12.023 10.1287/inte.1090.0492 10.1016/j.ejor.2015.08.051 10.1007/s10589-017-9946-1 10.1109/ACCESS.2020.2972018 10.3390/min9020107 10.24425/gsm.2019.128518 10.1007/s42461-020-00285-8 10.1179/1743286315Y.0000000026 10.1007/s00291-021-00618-z 10.1016/j.asoc.2021.107406 10.17159/2411-9717/2018/v118n12a5 10.1287/INTE.2020.1063 10.1016/j.resourpol.2019.101408 10.1287/inte.2020.1027 10.1016/j.cor.2020.105036 10.1109/ISAP.2005.1599245 10.1016/j.ejor.2014.08.020 10.1016/j.resourpol.2020.101809 10.1007/s12206-012-0411-x 10.1016/j.ejor.2021.01.011 10.1080/17480930.2021.1888395 10.1080/17480930.2019.1576576 10.24425/gsm.2020.133948 10.3390/met11111848 10.1016/j.ijmst.2016.11.014 10.1287/opre.2019.1965 10.3390/pr6110228 10.1080/17480930.2019.1584952 |
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| Keywords | Non-dominated sorting genetic algorithm II (NSGA-II) Underground backfilling mine Multi-objective integer programming Underground mine production scheduling |
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| References | Shabani-NaeeniFGhasemy YaghinRIntegrating data visibility decision in a multi-objective procurement transport planning under risk: A modified NSGA-IIAppl Soft Comput202110710.1016/j.asoc.2021.107406 Zhang X, Lin J (2017) Investigation of hydraulic-mechanical properties of paste backfill containing coal gangue-fly ash and its application in an underground coal mine. Energies 9(10). https://doi.org/10.3390/en10091309 AndreaBAkshayCAlexandraNMarcosGRaphaelGBarrick s turquoise ridge gold mine optimizes underground production scheduling operationsInterfaces202151210611810.1287/INTE.2020.1063 Ngatchou P, Zarei A, A El-Sharkawi M (2005) Pareto multi objective optimization. In: Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, p 84–91. https://doi.org/10.1109/ISAP.2005.1599245 GuXWangXLiuZZhaWXuXZhengMA Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production ProcessIEEE Access20208288472885810.1109/ACCESS.2020.2972018 BhartiPSMaheshwariSSharmaCMulti-objective optimization of electric-discharge machining process using controlled elitist nsga-iiJ Mech Sci Technol20122661875188310.1007/s12206-012-0411-x LamghariADimitrakopoulosRNetwork-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertaintyEur J Oper Res20162501273290343213510.1016/j.ejor.2015.08.0511346.90362 Wang X, Gu X, Liu Z, Wang Q, Xu X, Zheng M (2018) Production process optimization of metal mines considering economic benefit and resource efficiency using an nsga-ii model. Processes 6(11). https://doi.org/10.3390/pr6110228 MunozGEspinozaDGoycooleaMMorenoEQueyranneMLetelierORA study of the bienstock-zuckerberg algorithm: applications in mining and resource constrained project schedulingComput Optim Appl201869501534376895210.1007/s10589-017-9946-11403.90353 ForoughiSHamidiJKMonjeziMNehringMThe integrated optimization of underground stope layout designing and production scheduling incorporating a non-dominated sorting genetic algorithm (NSGA-II)Resour Policy201963May10.1016/j.resourpol.2019.101408 CinnaSMarcoSJuergenZSolution procedures for block selection and sequencing in flat-bedded potash underground minesOR Spectr202143240944010.1007/s00291-021-00618-z1476.90132 HouJLiGWangHHuNGenetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertaintyInt J Min Reclam Environ202034530732510.1080/17480930.2019.1584952 NewmanAMRubioECaroRWeintraubAEurekKA review of operations research in mine planningInterfaces201040322224510.1287/inte.1090.0492 HuangSLiGBen-AwuahEAfumBOHuNA robust mixed integer linear programming framework for underground cut-and-fill mining production schedulingInt J Min Reclam Environ202034639741410.1080/17480930.2019.1576576 WangHTenorioVLiGHouJHuNOptimization of trackless equipment scheduling in underground mines using genetic algorithmsMin Metall Explor20203751531154410.1007/s42461-020-00285-8 LetelierOREspinozaDGoycooleaMMorenoEMuñozGProduction scheduling for strategic open pit mine planning: A mixed-integer programming approachOper Res202068514251444416630510.1287/opre.2019.19651455.90069 Kopacz M, Malinowski L, Kaczmarzewski S, Kamiński P (2020) Optimizing mining production plan as a trade-off betweeresources utilization and economic targets in underground coal mines. Gospodarka Surowcami Mineralnymi Miner Resour Manag 36(4):49–74. https://doi.org/10.24425/gsm.2020.133948 SullivanDONewmanAExtraction and backfill scheduling in a complex underground mineInterfaces201444October20422110.1287/inte.2013.0730 Whittle G (2009) Misguided objectives that destroy value. In: Proceedings orebody modelling and strategic mine planning, p 97–101 CarpentierSGamacheMDimitrakopoulosRUnderground long-term mine production scheduling with integrated geological risk managementTrans Inst Min Metall Sect A Min Technol201612529310210.1179/1743286315Y.0000000026 YinYZhaoTZhangYTanYQiuYTaheriAJingYAn innovative method for placement of gangue backfilling material in steep underground coal minesMinerals20199210710.3390/min9020107 NesbittPBlakeLRLamasPGoycooleaMPagnoncelliBKNewmanABrickeyAUnderground mine scheduling under uncertaintyEur J Oper Res2021294134035210.1016/j.ejor.2021.01.011 Hou J, Li G, Hu N, Wang H (2019) Simultaneous integrated optimization for underground mine planning: Application and risk analysis of geological uncertainty in a gold deposit. Gospodarka Surowcami Mineralnymi Miner Resour Manag 35(2):153–174. https://doi.org/10.24425/gsm.2019.128518 Li N, Feng S, Ye H, Wang Q, Jia M, Wang L, Zhao S, Chen D (2021) Dispatch optimization model for haulage equipment between stopes based on mine short term resource planning. Metals 11(11). https://doi.org/10.3390/met11111848 AndreaBAkshayCAlexandraNMarcosGBarrick’s turquoise ridge gold mine optimizes underground production scheduling operationsINFORMS J Appl Anal202151210611810.1287/inte.2020.1027 Åstrand M, Johansson M, Zanarini A (2020) Underground mine scheduling of mobile machines using Constraint Programming and Large Neighborhood Search. Comput Oper Res 123. https://doi.org/10.1016/j.cor.2020.105036 SotoudehFNehringMKizilMKnightsPMousaviAProduction scheduling optimisation for sublevel stoping mines using mathematical programming: A review of literature and future directionsResour Policy202068July10.1016/j.resourpol.2020.101809 Sullivan DO, Newman A (2015) Optimization-based heuristics for underground mine scheduling. Eur J Oper Res 241(1):248–259. https://doi.org/10.1016/j.ejor.2014.08.020 FathollahzadehKAsadMWAMardanehECiglaMReview of solution methodologies for open pit mine production scheduling problemInt J Min Reclam Environ202135856459910.1080/17480930.2021.1888395 Zhang J, Zhang Q, Spearing AS, Miao X, Guo S, Sun Q (2017) Green coal mining technique integrating mining-dressing-gas draining-backfilling-mining. Int J Min Sci Technol 27(1):17–27. https://doi.org/10.1016/j.ijmst.2016.11.014 (sI: Special Issue on Ground Control in Mining in 2016) YuSZhengSGaoSYangJA multi-objective decision model for investment in energy savings and emission reductions in coal miningEur J Oper Res20172601335347361265910.1016/j.ejor.2016.12.0231402.90173 ÅstrandMJohanssonMGrebergJUnderground mine scheduling modelled as a flow shop: a review of relevant work and future challengesJ South Afr Inst Min Metall2018118121265127610.17159/2411-9717/2018/v118n12a5 606_CR1 B Andrea (606_CR10) 2021; 51 G Munoz (606_CR14) 2018; 69 606_CR30 S Carpentier (606_CR20) 2016; 125 AM Newman (606_CR4) 2010; 40 K Fathollahzadeh (606_CR8) 2021; 35 606_CR17 J Hou (606_CR23) 2020; 34 S Foroughi (606_CR27) 2019; 63 S Huang (606_CR19) 2020; 34 DO Sullivan (606_CR16) 2014; 44 606_CR32 S Cinna (606_CR11) 2021; 43 F Sotoudeh (606_CR3) 2020; 68 M Åstrand (606_CR18) 2018; 118 S Yu (606_CR28) 2017; 260 A Lamghari (606_CR6) 2016; 250 H Wang (606_CR25) 2020; 37 PS Bharti (606_CR26) 2012; 26 B Andrea (606_CR13) 2021; 51 606_CR29 X Gu (606_CR31) 2020; 8 P Nesbitt (606_CR15) 2021; 294 606_CR22 606_CR9 606_CR21 Y Yin (606_CR5) 2019; 9 F Shabani-Naeeni (606_CR12) 2021; 107 606_CR2 606_CR24 OR Letelier (606_CR7) 2020; 68 |
| References_xml | – reference: HuangSLiGBen-AwuahEAfumBOHuNA robust mixed integer linear programming framework for underground cut-and-fill mining production schedulingInt J Min Reclam Environ202034639741410.1080/17480930.2019.1576576 – reference: Kopacz M, Malinowski L, Kaczmarzewski S, Kamiński P (2020) Optimizing mining production plan as a trade-off betweeresources utilization and economic targets in underground coal mines. Gospodarka Surowcami Mineralnymi Miner Resour Manag 36(4):49–74. https://doi.org/10.24425/gsm.2020.133948 – reference: FathollahzadehKAsadMWAMardanehECiglaMReview of solution methodologies for open pit mine production scheduling problemInt J Min Reclam Environ202135856459910.1080/17480930.2021.1888395 – reference: CinnaSMarcoSJuergenZSolution procedures for block selection and sequencing in flat-bedded potash underground minesOR Spectr202143240944010.1007/s00291-021-00618-z1476.90132 – reference: Sullivan DO, Newman A (2015) Optimization-based heuristics for underground mine scheduling. Eur J Oper Res 241(1):248–259. https://doi.org/10.1016/j.ejor.2014.08.020 – reference: Zhang J, Zhang Q, Spearing AS, Miao X, Guo S, Sun Q (2017) Green coal mining technique integrating mining-dressing-gas draining-backfilling-mining. Int J Min Sci Technol 27(1):17–27. https://doi.org/10.1016/j.ijmst.2016.11.014 (sI: Special Issue on Ground Control in Mining in 2016) – reference: GuXWangXLiuZZhaWXuXZhengMA Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production ProcessIEEE Access20208288472885810.1109/ACCESS.2020.2972018 – reference: WangHTenorioVLiGHouJHuNOptimization of trackless equipment scheduling in underground mines using genetic algorithmsMin Metall Explor20203751531154410.1007/s42461-020-00285-8 – reference: LetelierOREspinozaDGoycooleaMMorenoEMuñozGProduction scheduling for strategic open pit mine planning: A mixed-integer programming approachOper Res202068514251444416630510.1287/opre.2019.19651455.90069 – reference: Ngatchou P, Zarei A, A El-Sharkawi M (2005) Pareto multi objective optimization. In: Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, p 84–91. https://doi.org/10.1109/ISAP.2005.1599245 – reference: SullivanDONewmanAExtraction and backfill scheduling in a complex underground mineInterfaces201444October20422110.1287/inte.2013.0730 – reference: MunozGEspinozaDGoycooleaMMorenoEQueyranneMLetelierORA study of the bienstock-zuckerberg algorithm: applications in mining and resource constrained project schedulingComput Optim Appl201869501534376895210.1007/s10589-017-9946-11403.90353 – reference: NesbittPBlakeLRLamasPGoycooleaMPagnoncelliBKNewmanABrickeyAUnderground mine scheduling under uncertaintyEur J Oper Res2021294134035210.1016/j.ejor.2021.01.011 – reference: Li N, Feng S, Ye H, Wang Q, Jia M, Wang L, Zhao S, Chen D (2021) Dispatch optimization model for haulage equipment between stopes based on mine short term resource planning. Metals 11(11). https://doi.org/10.3390/met11111848 – reference: Wang X, Gu X, Liu Z, Wang Q, Xu X, Zheng M (2018) Production process optimization of metal mines considering economic benefit and resource efficiency using an nsga-ii model. Processes 6(11). https://doi.org/10.3390/pr6110228 – reference: AndreaBAkshayCAlexandraNMarcosGRaphaelGBarrick s turquoise ridge gold mine optimizes underground production scheduling operationsInterfaces202151210611810.1287/INTE.2020.1063 – reference: HouJLiGWangHHuNGenetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertaintyInt J Min Reclam Environ202034530732510.1080/17480930.2019.1584952 – reference: CarpentierSGamacheMDimitrakopoulosRUnderground long-term mine production scheduling with integrated geological risk managementTrans Inst Min Metall Sect A Min Technol201612529310210.1179/1743286315Y.0000000026 – reference: Hou J, Li G, Hu N, Wang H (2019) Simultaneous integrated optimization for underground mine planning: Application and risk analysis of geological uncertainty in a gold deposit. Gospodarka Surowcami Mineralnymi Miner Resour Manag 35(2):153–174. https://doi.org/10.24425/gsm.2019.128518 – reference: LamghariADimitrakopoulosRNetwork-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertaintyEur J Oper Res20162501273290343213510.1016/j.ejor.2015.08.0511346.90362 – reference: Shabani-NaeeniFGhasemy YaghinRIntegrating data visibility decision in a multi-objective procurement transport planning under risk: A modified NSGA-IIAppl Soft Comput202110710.1016/j.asoc.2021.107406 – reference: Zhang X, Lin J (2017) Investigation of hydraulic-mechanical properties of paste backfill containing coal gangue-fly ash and its application in an underground coal mine. Energies 9(10). https://doi.org/10.3390/en10091309 – reference: Whittle G (2009) Misguided objectives that destroy value. In: Proceedings orebody modelling and strategic mine planning, p 97–101 – reference: AndreaBAkshayCAlexandraNMarcosGBarrick’s turquoise ridge gold mine optimizes underground production scheduling operationsINFORMS J Appl Anal202151210611810.1287/inte.2020.1027 – reference: SotoudehFNehringMKizilMKnightsPMousaviAProduction scheduling optimisation for sublevel stoping mines using mathematical programming: A review of literature and future directionsResour Policy202068July10.1016/j.resourpol.2020.101809 – reference: BhartiPSMaheshwariSSharmaCMulti-objective optimization of electric-discharge machining process using controlled elitist nsga-iiJ Mech Sci Technol20122661875188310.1007/s12206-012-0411-x – reference: ÅstrandMJohanssonMGrebergJUnderground mine scheduling modelled as a flow shop: a review of relevant work and future challengesJ South Afr Inst Min Metall2018118121265127610.17159/2411-9717/2018/v118n12a5 – reference: YinYZhaoTZhangYTanYQiuYTaheriAJingYAn innovative method for placement of gangue backfilling material in steep underground coal minesMinerals20199210710.3390/min9020107 – reference: NewmanAMRubioECaroRWeintraubAEurekKA review of operations research in mine planningInterfaces201040322224510.1287/inte.1090.0492 – reference: ForoughiSHamidiJKMonjeziMNehringMThe integrated optimization of underground stope layout designing and production scheduling incorporating a non-dominated sorting genetic algorithm (NSGA-II)Resour Policy201963May10.1016/j.resourpol.2019.101408 – reference: Åstrand M, Johansson M, Zanarini A (2020) Underground mine scheduling of mobile machines using Constraint Programming and Large Neighborhood Search. Comput Oper Res 123. https://doi.org/10.1016/j.cor.2020.105036 – reference: YuSZhengSGaoSYangJA multi-objective decision model for investment in energy savings and emission reductions in coal miningEur J Oper Res20172601335347361265910.1016/j.ejor.2016.12.0231402.90173 – ident: 606_CR2 doi: 10.3390/en10091309 – volume: 44 start-page: 204 issue: October year: 2014 ident: 606_CR16 publication-title: Interfaces doi: 10.1287/inte.2013.0730 – volume: 260 start-page: 335 issue: 1 year: 2017 ident: 606_CR28 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2016.12.023 – volume: 40 start-page: 222 issue: 3 year: 2010 ident: 606_CR4 publication-title: Interfaces doi: 10.1287/inte.1090.0492 – volume: 250 start-page: 273 issue: 1 year: 2016 ident: 606_CR6 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2015.08.051 – volume: 69 start-page: 501 year: 2018 ident: 606_CR14 publication-title: Comput Optim Appl doi: 10.1007/s10589-017-9946-1 – volume: 8 start-page: 28847 year: 2020 ident: 606_CR31 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2972018 – volume: 9 start-page: 107 issue: 2 year: 2019 ident: 606_CR5 publication-title: Minerals doi: 10.3390/min9020107 – ident: 606_CR22 doi: 10.24425/gsm.2019.128518 – volume: 37 start-page: 1531 issue: 5 year: 2020 ident: 606_CR25 publication-title: Min Metall Explor doi: 10.1007/s42461-020-00285-8 – volume: 125 start-page: 93 issue: 2 year: 2016 ident: 606_CR20 publication-title: Trans Inst Min Metall Sect A Min Technol doi: 10.1179/1743286315Y.0000000026 – volume: 43 start-page: 409 issue: 2 year: 2021 ident: 606_CR11 publication-title: OR Spectr doi: 10.1007/s00291-021-00618-z – volume: 107 year: 2021 ident: 606_CR12 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107406 – volume: 118 start-page: 1265 issue: 12 year: 2018 ident: 606_CR18 publication-title: J South Afr Inst Min Metall doi: 10.17159/2411-9717/2018/v118n12a5 – volume: 51 start-page: 106 issue: 2 year: 2021 ident: 606_CR13 publication-title: Interfaces doi: 10.1287/INTE.2020.1063 – volume: 63 issue: May year: 2019 ident: 606_CR27 publication-title: Resour Policy doi: 10.1016/j.resourpol.2019.101408 – volume: 51 start-page: 106 issue: 2 year: 2021 ident: 606_CR10 publication-title: INFORMS J Appl Anal doi: 10.1287/inte.2020.1027 – ident: 606_CR17 doi: 10.1016/j.cor.2020.105036 – ident: 606_CR24 doi: 10.1109/ISAP.2005.1599245 – ident: 606_CR9 doi: 10.1016/j.ejor.2014.08.020 – volume: 68 issue: July year: 2020 ident: 606_CR3 publication-title: Resour Policy doi: 10.1016/j.resourpol.2020.101809 – volume: 26 start-page: 1875 issue: 6 year: 2012 ident: 606_CR26 publication-title: J Mech Sci Technol doi: 10.1007/s12206-012-0411-x – volume: 294 start-page: 340 issue: 1 year: 2021 ident: 606_CR15 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2021.01.011 – volume: 35 start-page: 564 issue: 8 year: 2021 ident: 606_CR8 publication-title: Int J Min Reclam Environ doi: 10.1080/17480930.2021.1888395 – volume: 34 start-page: 397 issue: 6 year: 2020 ident: 606_CR19 publication-title: Int J Min Reclam Environ doi: 10.1080/17480930.2019.1576576 – ident: 606_CR21 doi: 10.24425/gsm.2020.133948 – ident: 606_CR30 doi: 10.3390/met11111848 – ident: 606_CR1 doi: 10.1016/j.ijmst.2016.11.014 – volume: 68 start-page: 1425 issue: 5 year: 2020 ident: 606_CR7 publication-title: Oper Res doi: 10.1287/opre.2019.1965 – ident: 606_CR29 doi: 10.3390/pr6110228 – volume: 34 start-page: 307 issue: 5 year: 2020 ident: 606_CR23 publication-title: Int J Min Reclam Environ doi: 10.1080/17480930.2019.1584952 – ident: 606_CR32 |
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| Title | Optimization Production Scheduling of Underground Backfilling Mining Based on NSGA-II |
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