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Büyük Veriyle Kaynak ve Kapasite Kısıtları Altında Üretim Planlama ve Çizelgeleme

Yıl 2021, Cilt: 36 Sayı: 4, 759 - 770, 31.12.2021
https://doi.org/10.24988/ije.718638

Öz

Büyük veri, işletmelerin iç yapısında biriken veya çeşitli dış kaynaklardan toplanan verilerin derlenmesi, depolanması, düzenlenmesi ve analiz edilmesiyle anlamlı bilgiler ortaya çıkararak, işletmeler için fayda yaratabilmektedir. İşletmenin fonksiyonlarından biri olan üretim alanında veri derlemek bu verileri yapısal hale getirerek değer yaratmak büyük avantaj yaratmaktadır. Büyük veri setlerinin analiz edilerek üretim planlamasında kullanılması hammaddelerin başarılı bir şekilde çıktılara dönüşebilmesinde önemli rol oynamaktadır. Madencilik sektörü içinde birçok belirsizliği barındırması, sermaye yoğun bir iş olması nedeniyle uzun vadeli üretim planlamasına ihtiyaç duyan sektörlerden biridir. Öte yandan gelişen teknolojiyle beraber maden sahaları hakkında büyük veri setlerinin derlenebilmesi mümkün hale gelmektedir. Bu çalışma kapsamında açık ocak madenciliği yapan orta büyüklükte bir firmanın sahip olduğu kömür ocağında yaşanan sorunların çözülebilmesi amacıyla maden sahasından gerekli veri toplanarak uzun vadeli üretim planlaması ve çizelgelemesi yapılmıştır. Firmanın üretimini etkileyen kaynak ve kapasite kısıtları ve sahip olduğu cevherin özellikleri, literatürde yer alan tam sayılı üretim planlanma ve çizelgeleme matematiksel modellerine uyarlanmıştır. Kurulan matematiksel model Python programlama diliyle çözülmüştür. Çalışmanın sonucunda firmanın ilgili kömür ocağındaki 5 yıllık üretiminin çizelgesine ve toplamda elde edeceği kârın net bugünkü değerine ulaşılmıştır. Çalışma yararlandığı büyük veri setleri ve matematiksel model sayesinde ortaya çıkardığı plan ile firmaya gelecek için politikalar geliştirebilme imkânı sunmuştur.

Kaynakça

  • Amankwah, H. (2011), “Mathematical Optimization Models And Methods For Open-Pit Mining”, Phd Thesis. Linköping University Electronic Press, 1-133.
  • Bley, A., Boland, N., Fricke, C., & Froyland, G. (2010). A strengthened formu-lation and cutting planes for the open pit mine production scheduling problem. Computers & Operations Research, 37(9), 1641-1647.
  • Boland, N., Dumitrescu, I., Froyland, G., & Gleixner, A. M. (2009). LP-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity. Computers & Opera-tions Research, 36(4), 1064-1089.
  • Boucher, A., & Dimitrakopoulos, R. (2009). Block simulation of multiple cor-related variables. Mathematical Geosciences, 41(2), 215-237.
  • Caccetta, L. Ve Hill, S. (2003), “An Application Of Branch And Cut To Open Pit Mine Scheduling”, Journal Of Global Optimization, 27(2–3), 349–365.
  • Dantzig, G. (1948), “Programming In A Linear Structure”, Washington.
  • Dantzig, G. ve Wolfe, P. (1960), “Decomposition Principle For Linear Prog-rams”, Operations Research, 8(1), 101-111.
  • Dagdelen, K., & Johnson, T. (1986). Optimum open pit mine production sche-duling by Lagrangian parameterization.In 19th APCOM, University Park, PA (pp. 127–141).
  • Denby, B. ve Schofield, D. (1994), “Open-Pit Design And Scheduling By Use Of Genetic Algorithms”, Transactionsof The Institution Of Mining And Metal-lurgy, Section A: Mining Industry, 103, A21–A26.
  • Denby, B., Schofield, D., Mcclarnon, D. J., Williams, M.& Walsha, T. (1998), “Hazard Awareness Training For Mining Situations Using Virtual Rea-lity”, In Computer Applications In The Minerals Industries. International Sympo-sium (Pp. 695-705).
  • Dimitrakopoulos, R. (1998), “Conditional Simulation Algorithms For Model-ling Orebody Uncertainty In Open Pit Optimisation”, International Journal Of Surface Mining, Reclamation And Environment, 12(4), 173-179.
  • Dimitrakopoulos, R.ve Ramazan, S. (2004), “Uncertainty Based Production Scheduling In Open Pit Mining”, SME Transactions, 316, 106-112.
  • Dimitrakopoulos, R., Mustapha, H., & Gloaguen, E. (2010). High-order statis-tics of spatial random fields: exploring spatial cumulants for modeling complex non-Gaussian and non-linear phenomena. Mathematical Geoscien-ces, 42(1), 65-99.
  • Dimitrakopoulos, R. (2011), “Stochastic Optimization For Strategic Mine Planning: A Decade Of Developments”, Journal Of Mining Science, 47(2), 138-150.
  • Dowd, P. ve Onur A. H. (1993), “Open-Pit Optimization, Part 1: Optimal Open-Pit Design”, Mining Tech.: IMM Trans. Sect., A102-A95–A104.
  • Dowd, P. (1994), “The Optimal Design Of Quarries”, Geological Society, Lon-don, Special Publications, 79(1), 141-155.
  • Fricke, C. (2006). Applications of integer programming in open pit mining, PhD Thesis The University of Melbourne, 1-239.
  • Gaupp, M. P. (2008). Methods for improving the tractability of the block sequencing problem for open pit mining. Colorado School Of Mınes Golden, 1-159.
  • Gershon, M. E., & Murphy, F. H. (1989). Optimizing single hole mine cuts by dynamic programming. European journal of operational research, 38(1), 56-62.
  • Godoy, M. ve Dimitrakopoulos, R. (2004), “Managing Risk And Waste Mi-ning İn Long-Term Production Scheduling Of Open-Pit Mines”, SME Tran-sactions, Vol 316, 43-50.
  • Golamnejad, J., Osanloo, M. & Karimi, B. (2006). A chance-constrained prog-ramming approach for open pit long-term production scheduling in stoc-hastic environments. Journal of the Southern African Institute of Mining and Metallurgy, 106(2), 105-114.
  • Grieco, N., & Dimitrakopoulos, R. (2007). Managing grade risk in stope de-sign optimisation: probabilistic mathematical programming model and application in sublevel stoping. Mining technology, 116(2), 49-57.
  • Hochbaum, D. ve Chen, A. (2000), “Performance Analysis And Best İmple-mentations Of Old And New Algorithms For The Open-Pit Mining Prob-lem”, Operations Research, 48(6) 894–914.
  • Hustrulid, W., & Kuchta, M. (2006). Open pit mine planning and design. Vol 1. Fundamentals; Vol. 2. CSMine software package and orebodey case examples. 2nd.
  • Kumral M. (2010), “Robust Stochastic Mine Production Scheduling”, Eng Op-tim ;42(6):567–79.
  • Kumral M. (2010), “Production Planning Of Mines: Optimisation Of Block Sequencing And Destination”, Int J Min Reclam Environ;26(2):93–103.
  • Lamghari, A. ve Dimitrakopoulos, R. (2012), “A Diversified Tabu Search Approach For The Open-Pit Mine Production Scheduling Problem With Metal Uncertainty”, European Journal Of Operational Research, 222(3), 642-652.
  • Lerchs, H. ve Grossmann, I. (1965), “Optimum Design Of Open-Pit Mines”, Canadian Mining Metallurgical Bull. 58 17–24.
  • Johnson, T. (1968), “Optimum Open Pit Mine Production Scheduling” (No. ORC-68-11). California Univ Berkeley Operatıons Research Center,1-131.
  • Newman, A., Rubio, E., Caro, R., Weintraub, A. & Eurek, K. (2010), “A Re-view Of Operations Research In Mine Planning”, Interfaces, 40(3), 222-245.
  • Onur, A. H. ve Dowd, P. (1993), “Open-Pit Optimization- Part 2: Production Scheduling And Inclusion Of Roadways”, Trans Inst Mın Metall Sect A Mın Ind., 102, 105-113.
  • Ramazan, S. ve Dimitrakopoulos, R. (2004), “Recent Applications Of Operati-ons Research And Efficient MIP Formulations In Open Pit Mining”, Society Of Mining, Metallurgy, And Exploration, Inc. Transactions, 73-78.
  • Ramazan, S. , Dagdelen, K. & Johnson, T. (2005), “Fundamental Tree Algo-rithm İn Optimising Production Scheduling For Open Pit Mine Design”, Mining Technology, 114(1), 45-54.
  • Ramazan, S. (2007), “The New Fundamental Tree Algorithm For Production Scheduling Of Open Pit Mines”, European Journal Of Operational Rese-arch,177(2), 1153-1166.
  • Ramazan, S. ve Dimitrakopoulos, R. (2012), “Production Scheduling With Uncertain Supply: A New Solution To The Open Pit Mining Problem” Op-timization And Engineering, 1-20.
  • Ravenscroft, P. J. (1992). “Risk Analysis For Mine Scheduling By Conditional Simulation”, Transactions Of The Institution Of Mining And Metallurgy. Sec-tion A. Mining Industry, 101, A104-A108.
  • Samavati, M., Essam, D., Nehring, M. &, Sarker, R., (2018), “Open-Pit Mine Production Planning And Scheduling: A Research Agenda”, In Data And Decision Sciences In Action (Pp. 221-226). Springer, Cham.
  • Scheidt, C., & Caers, J. (2009). Representing spatial uncertainty using distan-ces and kernels. Mathematical Geosciences, 41(4), 397-419.
  • Seymour, R. S., & Bradford, D. F. (1995). Respiration of amphibian eggs. Phy-siological zoology, 68(1), 1-25.
  • Shishvan, M. S.& Sattarvand, J. (2015). “Long Term Production Planning Of Open Pit Mines By Ant Colony Optimization”, European Journal Of Operati-onal Research, 240(3), 825-836.
  • Tolwinski, B. (1998), “Scheduling Production For Open Pit Mines”, In Compu-ter Applications In The Minerals Industries. International Symposium, (Pp. 651-662).
  • Tolwinski, B. & Golosinski, T. (1995), “Long Term Open Pit Scheduler”. Mine Planning And Equipment Selection 1995, 265-270.
  • Underwood, R. & Tolwinski, B. (1998), “A Mathematical Programming Vi-ewpoint For Solving The Ultimate Pit Problem”, European Journal Of Opera-tional Research, 107(1), 96–107.
  • Warton, D.I. (2008) Raw data graphing: an informative but under-utilized tool for the analysis of multivariate abundances. Austral Ecology 33, pp. 290-300.
  • Wright, E. A. (1989), “Dynamic Programming In Open Pit Mining Sequence Planning: A Case Study”, In Proceedings of the 21st International Application of Computers and Operations Research Symposium (APCOM’89), 415-422.
  • Whittle, J. 1988. Beyond optimisation in open pit design, Proc. Computer Appli-cations in the Mineral Industries, (ed. K. Fytas), 331–337; Rotterdam, A.A. Bal-kema.

Production planning and scheduling with big data under resource and capacity constraints

Yıl 2021, Cilt: 36 Sayı: 4, 759 - 770, 31.12.2021
https://doi.org/10.24988/ije.718638

Öz

Big data creates value and gains insight into knowledge via collecting, warehousing, organizing and analyzing the data that obtained from operations or external resources. Make benefit of big data analysis while production planning and management are essential to process raw materials in successful outputs. An effective production system avails to company and to society as a whole. All production processes need planning to improve productivity. One of those processes is mining. Mining which has two methods, as open pit mining and underground mining, is a capital intense production process and contains lots of uncertainty. However, advancing technology provides getting more information about the mining areas and helps to enable collecting and analyzing big data. In this study, mine planning and scheduling models applied to a medium sized company which faces results of their unplanned production method in coal mine, most of the times. According to firm’s operational constraints and features of orebody, a mine planning and scheduling problem is designed and as solution method integer programming is used. That integer programming model is run by Python programming language and Gurobi optimizer. The optimizer showed the result of company’s net present value of total profit and information about mine scheduling for five years. Production plan which is a result of mathematical model driven by big data, provides the ability of generating strategies for future to company.

Kaynakça

  • Amankwah, H. (2011), “Mathematical Optimization Models And Methods For Open-Pit Mining”, Phd Thesis. Linköping University Electronic Press, 1-133.
  • Bley, A., Boland, N., Fricke, C., & Froyland, G. (2010). A strengthened formu-lation and cutting planes for the open pit mine production scheduling problem. Computers & Operations Research, 37(9), 1641-1647.
  • Boland, N., Dumitrescu, I., Froyland, G., & Gleixner, A. M. (2009). LP-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity. Computers & Opera-tions Research, 36(4), 1064-1089.
  • Boucher, A., & Dimitrakopoulos, R. (2009). Block simulation of multiple cor-related variables. Mathematical Geosciences, 41(2), 215-237.
  • Caccetta, L. Ve Hill, S. (2003), “An Application Of Branch And Cut To Open Pit Mine Scheduling”, Journal Of Global Optimization, 27(2–3), 349–365.
  • Dantzig, G. (1948), “Programming In A Linear Structure”, Washington.
  • Dantzig, G. ve Wolfe, P. (1960), “Decomposition Principle For Linear Prog-rams”, Operations Research, 8(1), 101-111.
  • Dagdelen, K., & Johnson, T. (1986). Optimum open pit mine production sche-duling by Lagrangian parameterization.In 19th APCOM, University Park, PA (pp. 127–141).
  • Denby, B. ve Schofield, D. (1994), “Open-Pit Design And Scheduling By Use Of Genetic Algorithms”, Transactionsof The Institution Of Mining And Metal-lurgy, Section A: Mining Industry, 103, A21–A26.
  • Denby, B., Schofield, D., Mcclarnon, D. J., Williams, M.& Walsha, T. (1998), “Hazard Awareness Training For Mining Situations Using Virtual Rea-lity”, In Computer Applications In The Minerals Industries. International Sympo-sium (Pp. 695-705).
  • Dimitrakopoulos, R. (1998), “Conditional Simulation Algorithms For Model-ling Orebody Uncertainty In Open Pit Optimisation”, International Journal Of Surface Mining, Reclamation And Environment, 12(4), 173-179.
  • Dimitrakopoulos, R.ve Ramazan, S. (2004), “Uncertainty Based Production Scheduling In Open Pit Mining”, SME Transactions, 316, 106-112.
  • Dimitrakopoulos, R., Mustapha, H., & Gloaguen, E. (2010). High-order statis-tics of spatial random fields: exploring spatial cumulants for modeling complex non-Gaussian and non-linear phenomena. Mathematical Geoscien-ces, 42(1), 65-99.
  • Dimitrakopoulos, R. (2011), “Stochastic Optimization For Strategic Mine Planning: A Decade Of Developments”, Journal Of Mining Science, 47(2), 138-150.
  • Dowd, P. ve Onur A. H. (1993), “Open-Pit Optimization, Part 1: Optimal Open-Pit Design”, Mining Tech.: IMM Trans. Sect., A102-A95–A104.
  • Dowd, P. (1994), “The Optimal Design Of Quarries”, Geological Society, Lon-don, Special Publications, 79(1), 141-155.
  • Fricke, C. (2006). Applications of integer programming in open pit mining, PhD Thesis The University of Melbourne, 1-239.
  • Gaupp, M. P. (2008). Methods for improving the tractability of the block sequencing problem for open pit mining. Colorado School Of Mınes Golden, 1-159.
  • Gershon, M. E., & Murphy, F. H. (1989). Optimizing single hole mine cuts by dynamic programming. European journal of operational research, 38(1), 56-62.
  • Godoy, M. ve Dimitrakopoulos, R. (2004), “Managing Risk And Waste Mi-ning İn Long-Term Production Scheduling Of Open-Pit Mines”, SME Tran-sactions, Vol 316, 43-50.
  • Golamnejad, J., Osanloo, M. & Karimi, B. (2006). A chance-constrained prog-ramming approach for open pit long-term production scheduling in stoc-hastic environments. Journal of the Southern African Institute of Mining and Metallurgy, 106(2), 105-114.
  • Grieco, N., & Dimitrakopoulos, R. (2007). Managing grade risk in stope de-sign optimisation: probabilistic mathematical programming model and application in sublevel stoping. Mining technology, 116(2), 49-57.
  • Hochbaum, D. ve Chen, A. (2000), “Performance Analysis And Best İmple-mentations Of Old And New Algorithms For The Open-Pit Mining Prob-lem”, Operations Research, 48(6) 894–914.
  • Hustrulid, W., & Kuchta, M. (2006). Open pit mine planning and design. Vol 1. Fundamentals; Vol. 2. CSMine software package and orebodey case examples. 2nd.
  • Kumral M. (2010), “Robust Stochastic Mine Production Scheduling”, Eng Op-tim ;42(6):567–79.
  • Kumral M. (2010), “Production Planning Of Mines: Optimisation Of Block Sequencing And Destination”, Int J Min Reclam Environ;26(2):93–103.
  • Lamghari, A. ve Dimitrakopoulos, R. (2012), “A Diversified Tabu Search Approach For The Open-Pit Mine Production Scheduling Problem With Metal Uncertainty”, European Journal Of Operational Research, 222(3), 642-652.
  • Lerchs, H. ve Grossmann, I. (1965), “Optimum Design Of Open-Pit Mines”, Canadian Mining Metallurgical Bull. 58 17–24.
  • Johnson, T. (1968), “Optimum Open Pit Mine Production Scheduling” (No. ORC-68-11). California Univ Berkeley Operatıons Research Center,1-131.
  • Newman, A., Rubio, E., Caro, R., Weintraub, A. & Eurek, K. (2010), “A Re-view Of Operations Research In Mine Planning”, Interfaces, 40(3), 222-245.
  • Onur, A. H. ve Dowd, P. (1993), “Open-Pit Optimization- Part 2: Production Scheduling And Inclusion Of Roadways”, Trans Inst Mın Metall Sect A Mın Ind., 102, 105-113.
  • Ramazan, S. ve Dimitrakopoulos, R. (2004), “Recent Applications Of Operati-ons Research And Efficient MIP Formulations In Open Pit Mining”, Society Of Mining, Metallurgy, And Exploration, Inc. Transactions, 73-78.
  • Ramazan, S. , Dagdelen, K. & Johnson, T. (2005), “Fundamental Tree Algo-rithm İn Optimising Production Scheduling For Open Pit Mine Design”, Mining Technology, 114(1), 45-54.
  • Ramazan, S. (2007), “The New Fundamental Tree Algorithm For Production Scheduling Of Open Pit Mines”, European Journal Of Operational Rese-arch,177(2), 1153-1166.
  • Ramazan, S. ve Dimitrakopoulos, R. (2012), “Production Scheduling With Uncertain Supply: A New Solution To The Open Pit Mining Problem” Op-timization And Engineering, 1-20.
  • Ravenscroft, P. J. (1992). “Risk Analysis For Mine Scheduling By Conditional Simulation”, Transactions Of The Institution Of Mining And Metallurgy. Sec-tion A. Mining Industry, 101, A104-A108.
  • Samavati, M., Essam, D., Nehring, M. &, Sarker, R., (2018), “Open-Pit Mine Production Planning And Scheduling: A Research Agenda”, In Data And Decision Sciences In Action (Pp. 221-226). Springer, Cham.
  • Scheidt, C., & Caers, J. (2009). Representing spatial uncertainty using distan-ces and kernels. Mathematical Geosciences, 41(4), 397-419.
  • Seymour, R. S., & Bradford, D. F. (1995). Respiration of amphibian eggs. Phy-siological zoology, 68(1), 1-25.
  • Shishvan, M. S.& Sattarvand, J. (2015). “Long Term Production Planning Of Open Pit Mines By Ant Colony Optimization”, European Journal Of Operati-onal Research, 240(3), 825-836.
  • Tolwinski, B. (1998), “Scheduling Production For Open Pit Mines”, In Compu-ter Applications In The Minerals Industries. International Symposium, (Pp. 651-662).
  • Tolwinski, B. & Golosinski, T. (1995), “Long Term Open Pit Scheduler”. Mine Planning And Equipment Selection 1995, 265-270.
  • Underwood, R. & Tolwinski, B. (1998), “A Mathematical Programming Vi-ewpoint For Solving The Ultimate Pit Problem”, European Journal Of Opera-tional Research, 107(1), 96–107.
  • Warton, D.I. (2008) Raw data graphing: an informative but under-utilized tool for the analysis of multivariate abundances. Austral Ecology 33, pp. 290-300.
  • Wright, E. A. (1989), “Dynamic Programming In Open Pit Mining Sequence Planning: A Case Study”, In Proceedings of the 21st International Application of Computers and Operations Research Symposium (APCOM’89), 415-422.
  • Whittle, J. 1988. Beyond optimisation in open pit design, Proc. Computer Appli-cations in the Mineral Industries, (ed. K. Fytas), 331–337; Rotterdam, A.A. Bal-kema.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Makaleler
Yazarlar

Burcu Akın 0000-0001-6665-3213

Göktürk Akın 0000-0003-4986-3993

Yayımlanma Tarihi 31 Aralık 2021
Gönderilme Tarihi 12 Nisan 2020
Kabul Tarihi 5 Eylül 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 36 Sayı: 4

Kaynak Göster

APA Akın, B., & Akın, G. (2021). Büyük Veriyle Kaynak ve Kapasite Kısıtları Altında Üretim Planlama ve Çizelgeleme. İzmir İktisat Dergisi, 36(4), 759-770. https://doi.org/10.24988/ije.718638

İzmir İktisat Dergisi
TR-DİZİN, DOAJ, EBSCO, ERIH PLUS, Index Copernicus, Ulrich’s Periodicals Directory, EconLit, Harvard Hollis, Google Scholar, OAJI, SOBIAD, CiteFactor, OJOP, Araştırmax, WordCat, OpenAIRE, Base, IAD, Academindex
tarafından taranmaktadır.

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