Reliability analysis of production schedule in multi-element deposits under grade-tonnage uncertainty with multi-destinations for the run of mine material
|
摘要
Blending contributes to create an opportunity to achieve the target feed material and helps optimize the run of mine(ROM) material. In addition, it allows mixing different materials and achieving a wide range of quality. Then, multiple destinations can be considered for ROM. The present study seeks to develop a Mixed Integer Programming(MIP) model for the production scheduling of an iron ore mine. Based on this model, different destinations are considered for mine products with different specifications. To this aim,ten scenarios were considered with a number of different destinations. The results indicated that the maximum NPV in the multiple product scenarios is about 15% more than that of the single product scenarios. Further, 30 realizations were used to evaluate the effect of grade and tonnage uncertainty. The reliability of the production plan was approximately 61.07% and 62.81% in the first and second period of exploitation, respectively.
Blending contributes to create an opportunity to achieve the target feed material and helps optimize the run of mine(ROM) material. In addition, it allows mixing different materials and achieving a wide range of quality. Then, multiple destinations can be considered for ROM. The present study seeks to develop a Mixed Integer Programming(MIP) model for the production scheduling of an iron ore mine. Based on this model, different destinations are considered for mine products with different specifications. To this aim,ten scenarios were considered with a number of different destinations. The results indicated that the maximum NPV in the multiple product scenarios is about 15% more than that of the single product scenarios. Further, 30 realizations were used to evaluate the effect of grade and tonnage uncertainty. The reliability of the production plan was approximately 61.07% and 62.81% in the first and second period of exploitation, respectively.
Blending contributes to create an opportunity to achieve the target feed material and helps optimize the run of mine(ROM) material. In addition, it allows mixing different materials and achieving a wide range of quality. Then, multiple destinations can be considered for ROM. The present study seeks to develop a Mixed Integer Programming(MIP) model for the production scheduling of an iron ore mine. Based on this model, different destinations are considered for mine products with different specifications. To this aim,ten scenarios were considered with a number of different destinations. The results indicated that the maximum NPV in the multiple product scenarios is about 15% more than that of the single product scenarios. Further, 30 realizations were used to evaluate the effect of grade and tonnage uncertainty. The reliability of the production plan was approximately 61.07% and 62.81% in the first and second period of exploitation, respectively.
引文
[1]Peng FF,Walters AD,Geer MR,Leonard JW.Evaluation and prediction of optimum cleaning results.Chap 18 in Coal Preparation.New York:AIME,4th ed;1979.p.18-53.
[2]Fytas K,Calder P.A computerized model of open pit short and long range production scheduling.In:Application of computers and operations research in the mineral industry,Pennsylvania;1986.p.109-19.
[3]White W,Olson J.Computer-based dispatching in mines with concurrent operating objectives.J Min Eng 1986;38:1045-54.
[4]Sundar DK,Acharya D.Blast schedule planning and shift wise production scheduling of an opencast iron ore mine.J Comput Ind Eng 1995;28:927-32.
[5]Temeng VA,Francis OO,Frendewey JO.Real-time truck dispatching using a transportation algorithm.Int J Surf Min,Rec Environ 1997;11:203-7.
[6]Gholamnejad J,Osanloo M,Khoram E.A chance constrained integer programming model for open pit long-term production planning.Int J Eng,Trans A:Basic 2008;21:407-18.
[7]L’Heureux G,Gamache M,Soumis F.Mixed integer programming model for short term planning in open-pit mines.Min Technol 2013;122:101-9.
[8]Smith ML,Wicks SJ.Medium-term production scheduling of the lumwana mining complex.Interfaces(Providence)2014;44:176-94.
[9]Smith ML.Integrating conditional simulation and stochastic programming:an application in production scheduling.In:Application of computers and operations research in the mineral industry,Beijing;2001.p.203-8.
[10]Rehman S,Asad MWA.A mixed integer linear programming model for short range production scheduling of cement quarry operations.Asia-Pacific J Oper Res 2010;27:315-33.
[11]Zuckerberg M,Vanderiet J,Malajczuk W,Stone P.Optimal life-of-mine scheduling for a bauxite mine.J Min Sci 2001;47:158-65.
[12]Askari-Nasab H,Eivazy H,Tabesh M,Badiozamani MM.A mathematical programming model for open pit short-term production scheduling.In:SMEannual meeting,Denver;2011.p.1-8.
[13]Rahmanpour M,Osanloo M.Pit limit determination considering blending requirements.In:Mine planning and equipment selection,New Delhi;2012.p.564-75.
[14]Kumral M,Dowd PA.Short term mine production scheduling for industrial mineral using multi objective simulated annealing.In:Application of computers and operations research in the mineral industry,Alaska;2002.p.731-37.
[15]Samanta B,Bhattacherjee A,Ganguli R.A genetic algorithms approach for grade control planning in a bauxite deposit.In:Application of computers and operation research in the mineral industries,Tucson;2005.p.334-42.
[16]Souza MJF,Coelho IM,Ribas S,Santos HG,Merschmann LHC.A hybrid heuristic algorithm for the open-pit-mining operational planning problem.Eur J Oper Res 2010;207:1041-51.
[17]Asad MWA.A heuristic approach to long-range production planning of cement quarry operations.Prod Plan Control 2011;22:353-64.
[18]Sari YA,Kumral M.Mine production scheduling for poly-metallic mineral deposits:extension to multiple processes.Int J Plan Sched 2015;2:134-53.
[19]Montiel L,Dimitrakopoulos R.Optimizing mining complexes with multiple processing and transportation alternatives:an uncertainty-based approach.Eur J Oper Res 2015;247:166-78.
[20]Goodfellow RC,Dimitrakopoulos R.Global optimization of open pit mining complexes with uncertainty.Appl Soft Comput 2016;40:292-304.
[21]Jelvez E,Morales N,Penard PN,Peypouquet J,Reyes P.Aggregation heuristic for the open-pit block scheduling problem.Eur J Oper Res 2016;249:1169-77.
[22]Lamghari A,Dimitrakopoulos R.Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty.Eur J Oper Res 2016;250:273-90.
[23]Matamoros MEV,Dimitrakopoulos R.Stochastic short-term mine production schedule accounting for fleet allocation,operational considerations and blending restrictions.Eur J Oper Res 2016;255:911-21.
[24]Jamshidi M,Osanloo.M.Determination of block economic value in multielement deposits.In:Conference on computer applications in the minerals industries,Istanbul;2016.p.1-9.
[25]Jamshidi M,Osanloo.M.UPL determination of multi-element deposits with grade uncertainty using a new block economic value calculation approach.JMin Environ 2018;9:61-72.
[26]Moren E,Rezakhah M,Newman A,Ferreira F.Linear models for stockpiling in open-pit mine production scheduling problems.Eur J Oper Res2017;260:212-21.
[27]Rahmanpour M,Osanloo M.Optimizing short-term production plan using a portfolio optimization model.REM-Int Eng J 2017;70:109-16.
[28]Mohammadi S,Kakaie R,Ataei M,Pourzamani E.Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm(ICA).Resour Policy2017;51:39-48.
[29]Dimitrakopoulos R.Conditional simulation algorithms for modelling orebody uncertainty in open pit optimisation.Int J Surf Mining,Reclam Environ1998;12:173-9.
[30]Journel AG.Fundamentals of geostatistics in five lessons.Washington,D.C.:American Geophysical Union;1989.
[31]Journel AG,Huijbregts C.Mining geostatistics.London:Academic Press;1978.
[32]Dowd PA.A review of recent developments in geostatistics.Comput Geosci1991;17:1481-500.
[33]Widijanto E.Operational reliability of an open pit mine-case study in Chuquicamata mine.Chile:University of Chile;2010.
[2]Fytas K,Calder P.A computerized model of open pit short and long range production scheduling.In:Application of computers and operations research in the mineral industry,Pennsylvania;1986.p.109-19.
[3]White W,Olson J.Computer-based dispatching in mines with concurrent operating objectives.J Min Eng 1986;38:1045-54.
[4]Sundar DK,Acharya D.Blast schedule planning and shift wise production scheduling of an opencast iron ore mine.J Comput Ind Eng 1995;28:927-32.
[5]Temeng VA,Francis OO,Frendewey JO.Real-time truck dispatching using a transportation algorithm.Int J Surf Min,Rec Environ 1997;11:203-7.
[6]Gholamnejad J,Osanloo M,Khoram E.A chance constrained integer programming model for open pit long-term production planning.Int J Eng,Trans A:Basic 2008;21:407-18.
[7]L’Heureux G,Gamache M,Soumis F.Mixed integer programming model for short term planning in open-pit mines.Min Technol 2013;122:101-9.
[8]Smith ML,Wicks SJ.Medium-term production scheduling of the lumwana mining complex.Interfaces(Providence)2014;44:176-94.
[9]Smith ML.Integrating conditional simulation and stochastic programming:an application in production scheduling.In:Application of computers and operations research in the mineral industry,Beijing;2001.p.203-8.
[10]Rehman S,Asad MWA.A mixed integer linear programming model for short range production scheduling of cement quarry operations.Asia-Pacific J Oper Res 2010;27:315-33.
[11]Zuckerberg M,Vanderiet J,Malajczuk W,Stone P.Optimal life-of-mine scheduling for a bauxite mine.J Min Sci 2001;47:158-65.
[12]Askari-Nasab H,Eivazy H,Tabesh M,Badiozamani MM.A mathematical programming model for open pit short-term production scheduling.In:SMEannual meeting,Denver;2011.p.1-8.
[13]Rahmanpour M,Osanloo M.Pit limit determination considering blending requirements.In:Mine planning and equipment selection,New Delhi;2012.p.564-75.
[14]Kumral M,Dowd PA.Short term mine production scheduling for industrial mineral using multi objective simulated annealing.In:Application of computers and operations research in the mineral industry,Alaska;2002.p.731-37.
[15]Samanta B,Bhattacherjee A,Ganguli R.A genetic algorithms approach for grade control planning in a bauxite deposit.In:Application of computers and operation research in the mineral industries,Tucson;2005.p.334-42.
[16]Souza MJF,Coelho IM,Ribas S,Santos HG,Merschmann LHC.A hybrid heuristic algorithm for the open-pit-mining operational planning problem.Eur J Oper Res 2010;207:1041-51.
[17]Asad MWA.A heuristic approach to long-range production planning of cement quarry operations.Prod Plan Control 2011;22:353-64.
[18]Sari YA,Kumral M.Mine production scheduling for poly-metallic mineral deposits:extension to multiple processes.Int J Plan Sched 2015;2:134-53.
[19]Montiel L,Dimitrakopoulos R.Optimizing mining complexes with multiple processing and transportation alternatives:an uncertainty-based approach.Eur J Oper Res 2015;247:166-78.
[20]Goodfellow RC,Dimitrakopoulos R.Global optimization of open pit mining complexes with uncertainty.Appl Soft Comput 2016;40:292-304.
[21]Jelvez E,Morales N,Penard PN,Peypouquet J,Reyes P.Aggregation heuristic for the open-pit block scheduling problem.Eur J Oper Res 2016;249:1169-77.
[22]Lamghari A,Dimitrakopoulos R.Network-flow based algorithms for scheduling production in multi-processor open-pit mines accounting for metal uncertainty.Eur J Oper Res 2016;250:273-90.
[23]Matamoros MEV,Dimitrakopoulos R.Stochastic short-term mine production schedule accounting for fleet allocation,operational considerations and blending restrictions.Eur J Oper Res 2016;255:911-21.
[24]Jamshidi M,Osanloo.M.Determination of block economic value in multielement deposits.In:Conference on computer applications in the minerals industries,Istanbul;2016.p.1-9.
[25]Jamshidi M,Osanloo.M.UPL determination of multi-element deposits with grade uncertainty using a new block economic value calculation approach.JMin Environ 2018;9:61-72.
[26]Moren E,Rezakhah M,Newman A,Ferreira F.Linear models for stockpiling in open-pit mine production scheduling problems.Eur J Oper Res2017;260:212-21.
[27]Rahmanpour M,Osanloo M.Optimizing short-term production plan using a portfolio optimization model.REM-Int Eng J 2017;70:109-16.
[28]Mohammadi S,Kakaie R,Ataei M,Pourzamani E.Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm(ICA).Resour Policy2017;51:39-48.
[29]Dimitrakopoulos R.Conditional simulation algorithms for modelling orebody uncertainty in open pit optimisation.Int J Surf Mining,Reclam Environ1998;12:173-9.
[30]Journel AG.Fundamentals of geostatistics in five lessons.Washington,D.C.:American Geophysical Union;1989.
[31]Journel AG,Huijbregts C.Mining geostatistics.London:Academic Press;1978.
[32]Dowd PA.A review of recent developments in geostatistics.Comput Geosci1991;17:1481-500.
[33]Widijanto E.Operational reliability of an open pit mine-case study in Chuquicamata mine.Chile:University of Chile;2010.