Development of a Long-term Mining Production Planning Model Considering Working Spaces for In-pit Crushers | ||
| Journal of Mining and Environment | ||
| مقاله 21، دوره 17، شماره 3، مرداد و شهریور 2026، صفحه 1145-1162 اصل مقاله (4.48 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.16077.3103 | ||
| نویسندگان | ||
| Fatemeh Asadi Ooriad؛ Javad Gholamnejad* ؛ Ali Dabagh | ||
| Department of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran | ||
| چکیده | ||
| Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical and operational constraints. Mathematical programming methods have proven suitable for optimizing mine production scheduling. Previous studies have addressed various aspects, including the timing of deployment and periodic relocation of in-pit crushers. Nevertheless, significant challenges remain in integrating the in-pit crusher problem with production planning. This paper introduces a new mixed-integer linear programming model for long-term open-pit mine production planning, incorporating constrained pit deepening to enforce predominantly lateral progression throughout the planning horizon. To achieve this, the number of active benches in each time period was reduced, thereby decreasing the need for equipment movement between working benches. Furthermore, with the horizontal progression of the pit, more workspace became available for deploying in-pit crushers, reducing equipment movement costs between benches and overall transportation costs, ultimately lowering the mine's operational expenses. Finally, the proposed model was implemented at the Miduk copper mine. The results demonstrated that the proposed model successfully achieved the expected objectives, resulting in a 52.45% improvement in reducing the number of active benches and regarding execution time reduction, the model showed a 53.32% improvement. | ||
| کلیدواژهها | ||
| long-term production scheduling؛ mathematical programming؛ active benches؛ practical plans؛ equipment movement | ||
| مراجع | ||
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[1]. Whittle, J. (1988). Beyond optimization in open pit design. Computer Applications in the Mineral Industry, Balkema, Rotterdam, 331, 337.
[2]. Newman, A. M., Rubio, E., Caro, R., Weintraub, A., & Eurek, K. (2010). A review of operations research in mine planning. Interfaces, 40(3), 222-245.
[3]. Osanloo, M., Gholamnejad, J., & Karimi, B. (2008). Long-term open pit mine production planning: a review of models and algorithms. International Journal of Mining, Reclamation and Environment, 22(1), 3-35.
[4]. Balkema, A., Kuchta, M., & Martin, R. (2006). Open Pit Mine Planning and Design. In: Taylor and Francis plc, London, The United Kingdom.
[5]. Lerchs, H. (1965). Optimum design of open-pit mines. CIM bulletin, 58(633), 47-54.
[6]. Caccetta, L., & Hill, S. P. (2003). An application of branch and cut to open pit mine scheduling. Journal of global optimization, 27, 349-365.
[7]. Perez Canto, S. (2011). Using 0/1 mixed integer linear programming to solve a reliability-centered problem of power plant preventive maintenance scheduling. Optimization and Engineering, 12, 333-347.
[8]. Johnson, T. B. (1968). Optimum open pit mine production scheduling: University of California, Berkeley.
[9]. Gershon, M. E. (1983). Mine scheduling optimization with mixed integer programming. Min. Eng. (Littleton, Colo.);(United States), 35(4).
[10]. Denby, B., & Schofield, D. (1994). Open-pit design and scheduling by use of genetic algorithms. Transactions of the Institution of Mining and Metallurgy. Section A. Mining Industry, 103.
[11]. Denby, B., Schofield, D., & Hunter, G. (1996). Genetic algorithms for open pit scheduling-extension into 3-dimentions. Paper presented at the 5th International Symposium on Mine Planning and Equipment Selection, Sao Paulo, Brazil.
[12]. Dimitrakopoulos, R., & Ramazan, S. (2004). Uncertainty based production scheduling in open pit mining. SME transactions, 316.
[13]. Gaupp, M. (2008). Methods for improving the tractability of the block sequencing problem for open pit mining: DTIC Document.
[14]. Shamsi, M., Pourrahimian, Y., & Rahmanpour, M. (2022). Optimisation of open-pit mine production scheduling considering optimum transportation system between truck haulage and semi-mobile in-pit crushing and conveying. International Journal of Mining, Reclamation and Environment, 36(2), 142-158.
[15]. Ramazan, S., & Dimitrakopoulos, R. (2018). Stochastic optimisation of long-term production scheduling for open pit mines with a new integer programming formulation. In Advances in applied strategic mine planning (pp. 139-153): Springer.
[16]. 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 & Operations Research, 36(4), 1064-1089.
[17]. Bley, A., Boland, N., Fricke, C., & Froyland, G. (2010). A strengthened formulation and cutting planes for the open pit mine production scheduling problem. Computers & Operations Research, 37(9), 1641-1647.
[18]. Cullenbine, C., Wood, R. K., & Newman, A. (2011). A sliding time window heuristic for open pit mine block sequencing. Optimization letters, 5, 365-377.
[19]. Sattarvand, J. (2012). Long-term open-pit planning by ant colony optimization. Aachen, Techn. Hochsch., Diss.,
[20]. Shishvan, M. S., & Sattarvand, J. (2015). Long term production planning of open pit mines by ant colony optimization. European Journal of Operational Research, 240(3), 825-836.
[21]. Gholamnejad, J., Lotfian, R., & Kasmaeeyazdi, S. (2020). A practical, long-term production scheduling model in open pit mines using integer linear programming. Journal of the Southern African Institute of Mining and Metallurgy, 120(12), 665-670.
[22]. Epstein, R., Gaete, S., Caro, F., Weintraub, A., Santibanez, P., & Catalan, J. (2003). Optimizing long term planning for underground copper mines. Paper presented at the Proc. Copper.
[23]. Indrayana, F., & Nainggolan, Y. A. (2021). Valuation for High-Risk Coal Mining Project Case Study: PT. Berau Coal Block Parapatan. In: Scopus.
[24]. Saragih, H. R. S. (2023). Modelling and optimization of open-pit mining operations. Universitat Politècnica de Catalunya.
[25]. Nehring, M., Knights, P., Kizil, M., & Hay, E. (2018). A comparison of strategic mine planning approaches for in-pit crushing and conveying, and truck/shovel systems. International journal of mining science and technology, 28(2), 205-214.
[26]. Dimitrakopoulos, R., Farrelly, C., & Godoy, M. (2002). Moving forward from traditional optimization: grade uncertainty and risk effects in open-pit design. Mining Technology, 111(1), 82-88.
[27]. Lagos, T., Armstrong, M., Homem-de-Mello, T., Lagos, G., & Sauré, D. (2020). A framework for adaptive open-pit mining planning under geological uncertainty. Optimization and Engineering, 1-36.
[28]. Martinez Lagunas, A. J., & Nik-Bakht, M. (2024). Process Mining, Modeling, and Management in Construction: A Critical Review of Three Decades of Research Coupled with a Current Industry Perspective. Journal of Construction Engineering and Management, 150(11), 04024158.
[29]. Mishra, A., Das, S. K., & Reddy, K. R. (2024). Potential use of coal mine overburden waste rock as sustainable geomaterial: review of properties and research challenges. Journal of Hazardous, Toxic, and Radioactive Waste, 28(1), 04023039.
[30]. Paricheh, M., Osanloo, M., & Rahmanpour, M. (2017). In-pit crusher location as a dynamic location problem. Journal of the Southern African Institute of Mining and Metallurgy, 117(6), 599-607.
[31]. Kamrani, A., Pourrahimian, Y., & Askari-Nasab, H. (2022). Long-term Mine Planning Optimization for IPCC-Based Open-Pit Mining Operations. Mining Optimization Laboratory, 1(780), 38.
[32]. Abbaspour, H. (2020). Transportation system selection in open-pit mines (Truck-Shovel and IPCC systems) based on the technical, economic, environmental, safety, and social (TEcESaS) indexes.
[33]. Paricheh, M., & Osanloo, M. (2020). Concurrent open-pit mine production and in-pit crushing–conveying system planning. Engineering optimization, 52(10), 1780-1795.
[34]. Al Habiba, N., Ben-Awuahb, E., & Askari-Nasaba, H. (2022). Review of Recent Developments in Short-Term Mine Planning and IPCC. Mining Optimization Laboratory, 1(780), 138.
[35]. Paricheh, M., Osanloo, M., & Rahmanpour, M. (2018). A heuristic approach for in-pit crusher and conveyor system’s time and location problem in large open-pit mining. International Journal of Mining, Reclamation and Environment, 32(1), 35-55.
[36]. Radlowski, J. K. (1988). In-pit crushing and conveying as an alternative to an all truck system in open pit mines. University of British Columbia.
[37]. Changzhi, Y. (2003). In-pit crushing and conveying system in Dexin pit copper haulage optimization for ore transport. Paper presented at the Fifth Large Open Pit Mining Conference.
[38]. Konak, G., Onur, AH, & Karakus, D. (2007). Selection of the optimum in-pit crusher location for an aggregate producer. Journal of the Southern African Institute of Mining and Metallurgy, 107(3), 161-166.
[39]. Turnbull, D., & Cooper, A. (2010). In-pit crushing and conveying (IPCC)-a tried and tested alternative to trucks: Part 1. AusIMM Bulletin (5), 60-64.
[40]. Atchison, T., & Morrison, D. (2011). In-pit crushing and conveying bench operations. Paper presented at the Proceedings of the tenth Iron Ore Conference.
[41]. Paricheh, M., & Osanloo, M. (2016). Determination of the optimum in-pit crusher location in open-pit mining under production and operating cost uncertainties. Paper presented at the 6th international conference on computer applications in the minerals industries.
[42]. Yarmuch, J., Epstein, R., Cancino, R., & Peña, J. C. (2017). Evaluating crusher system location in an open pit mine using Markov chains. International Journal of Mining, Reclamation and Environment, 31(1), 24-37.
[43]. Builes, C. A. J. (2017). A Mixed-Integer Programming Model for an In-Pit Crusher Conveyor Location Problem: Ecole Polytechnique, Montreal (Canada).
[44]. Abbaspour, H., Drebenstedt, C., Paricheh, M., & Ritter, R. (2019). Optimum location and relocation plan of semi-mobile in-pit crushing and conveying systems in open-pit mines by transportation problem. International Journal of Mining, Reclamation and Environment, 33(5), 297-317.
[45]. Shamsi, M., & Nehring, M. (2021). Determination of the optimal transition point between a truck and shovel system and a semi-mobile in-pit crushing and conveying system. Journal of the Southern African Institute of Mining and Metallurgy, 121(9), 497-504.
[46]. Shamsi, M., Pourrahimian, Y., & Rahmanpour, M. (2022). Optimisation of open-pit mine production scheduling considering optimum transportation system between truck haulage and semi-mobile in-pit crushing and conveying. International Journal of Mining, Reclamation and Environment, 36(2), 142-158.
[47]. Kamrani, A., Badiozamani, M. M., Pourrahimian, Y., & Askari-Nasab, H. (2024). Evaluating the semi-mobile in-pit crusher option through a two-step mathematical model. Resources Policy, 95, 105113.
[48]. Abbaspour, H., & Drebenstedt, C. (2023). Truck–Shovel vs. In-Pit Crushing and Conveying Systems in Open Pit Mines: A Technical Evaluation for Selecting the Most Effective Transportation System by System Dynamics Modeling. Logistics, 7(4), 92.
[49]. Findlay, L., & Dimitrakopoulos, R. (2024). Stochastic Optimization for Long-Term Planning of a Mining Complex with In-Pit Crushing and Conveying Systems. Mining, Metallurgy & Exploration, 41(4), 1677-1691.
[50]. Kamrani, A., Pourrahimian, Y., & Askari-Nasab, H. (2025). Semi-mobile in-pit crushing and conveying vs. truck-shovel systems: Long-term scheduling with road and conveyor networks integration. Expert Systems with Applications, 268, 126122.
[51]. Abalos, P., Brickey, A., & Goycoolea, M. (2025). Scheduling by Pushbacks: A Historical Review Ofoptimization Approaches for Strategic Open Pit Mineplanning. Available at SSRN 5138407.
[52]. Sjöberg, J. (1996). Large scale slope stability in open pit mining: a review.
Taheri, K., Hasanipanah, M., Golzar, S. B., & Majid, M. Z. A. (2017). A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Engineering with Computers, 33, 689-700.
[53]. Burt, C. N., & Caccetta, L. (2018). Equipment selection for mining: with case studies (Vol. 150): Springer.
Caccetta, L., & Hill, S. P. (2003). An application of branch and cut to open pit mine scheduling. Journal of global optimization, 27, 349-365.
[54]. Campos, P., Arroyo, C., & Morales, N. (2018). Application of optimized models through direct block scheduling in traditional mine planning. Journal of the Southern African Institute of Mining and Metallurgy, 118(4), 381-386.
[55]. Zahir, S., Sarker, R., & Al-Mahmud, Z. (2009). An interactive decision support system for implementing sustainable relocation strategies for adaptation to climate change: a multi-objective optimisation approach. International Journal of Mathematics in Operational Research, 1(3), 326-350.
[56]. Moradi Afrapoli, A., & Askari-Nasab, H. (2019). Mining fleet management systems: a review of models and algorithms. International Journal of Mining, Reclamation and Environment, 33(1), 42-60.
[57]. Osanloo, M., & Paricheh, M. (2020). In-pit crushing and conveying technology in open-pit mining operations: a literature review and research agenda. International Journal of Mining, Reclamation and Environment, 34(6), 430-457.
[58]. Upadhyay, S. P., & Askari-Nasab, H. (2019). Dynamic shovel allocation approach to short-term production planning in open-pit mines. International Journal of Mining, Reclamation and Environment, 33(1), 1-20.
[59]. Ataei, M., & Masir, R. N. (2020). A fuzzy DEMATEL based sustainable development index (FDSDI) in open pit mining–a case study. Rudarsko-geološko-naftni zbornik, 35(1).
[60]. Carter, R. A. (2010). Latest IPCC systems provide improved operational flexibility, higher capacity. Engineering and Mining Journal, 211(3), 42.
[61]. Whittle, C., Whitmarsh, L., Haggar, P., Morgan, P., & Parkhurst, G. (2019). User decision-making in transitions to electrified, autonomous, shared or reduced mobility. Transportation Research Part D: Transport and Environment, 71, 302-319.
[62]. Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization: Cambridge university press.
Burt, C. N., & Caccetta, L. (2014). Equipment selection for surface mining: a review. Interfaces, 44(2), 143-162.
[63]. Nocedal, J., & Wright, S. J. (2006). Quadratic programming. Numerical optimization, 448-492.
[64]. Floudas, C. A., & Pardalos, P. M. (2008). Encyclopedia of optimization: Springer Science & Business Media.
[65]. Newman, A. M., Yano, C. A., & Rubio, E. (2013). Mining above and below ground: Timing the transition. IIE Transactions, 45(8), 865-882.
[66]. Whittle, D. (2019). Underground mine plan optimisation. University of Melbourne, Parkville, Victoria, Australia.
[67]. Darling, P. (2011). SME mining engineering handbook (Vol. 1): SME.
[68]. Hustrulid, W. A., & Iverson, S. R. (2013). A new perimeter control blast design concept for underground metal/nonmetal drifting applications.
[69]. Williams, H. P. (2013). Model building in mathematical programming: John Wiley & Sons.
[70]. Queyranne, M., & Wolsey, L. A. (2017). Tight MIP formulations for bounded up/down times and interval-dependent start-ups. Mathematical Programming, 164(1), 129-155.
[71]. Wolsey, L. A., & Nemhauser, G. L. (1999). Integer and combinatorial optimization: John Wiley & Sons.
[72]. Montiel, L., & Dimitrakopoulos, R. (2015). Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach. European Journal of Operational Research, 247(1), 166-178.
[73]. Askari-Nasab, H., Awuah-Offei, K., & Eivazy, H. (2010). Large-scale open pit production scheduling using mixed integer linear programming. International Journal of Mining and Mineral Engineering, 2(3), 185-214.
[74]. Tashayoei, R. (2022). Museum of the Geological Survey of Iran, a Geotourism attraction. Journal of Tourism Hospitality Research, 1(2), 110.
[75]. Taheri, K., Hasanipanah, M., Golzar, S. B., & Majid, M. Z. A. (2017). A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Engineering with Computers, 33, 689-700.
[76]. Bai, X., Marcotte, D., Gamache, M., Gregory, D., & Lapworth, A. (2018). Automatic generation of feasible mining pushbacks for open pit strategic planning. Journal of the Southern African Institute of Mining and Metallurgy, 118(5), 514-530.
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آمار تعداد مشاهده مقاله: 343 تعداد دریافت فایل اصل مقاله: 48 |
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