مدل‌سازی تحلیلی مبتنی بر شبیه‌سازی برای برنامه‌ریزی‌های کوتاه‌مدت و بلندمدت در معدن روباز

نوع مقاله : یادداشت فنی

نویسندگان

1 گروه مهندسی صنایع، دانشکده‌ی مدیریت راهبردی، دانشگاه آیندگان، تنکابن، ایران.

2 گروه مهندسی صنایع، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران .

3 دانشکده‌ی مهندسی صنایع، دانشگاه صنعتی شریف، تهران، ایران.

چکیده

در نوشتار حاضر، یک چارچوب سلسله‌مراتبی دوسطحی مبتنی بر شبیه­سازی برای برنامه­ریزی تولید و ماشین‌آلات بارگذاری مواد در بزرگ‌‌ترین معدن روباز مس ایران ارائه شده است. برای حل مسئله‌ی برنامه­ریزی ماشین‌آلات در معدن، یک مسئله‌ی برنامه­ریزی فرمول­بندی‌شده و در چارچوب روش سلسله‌مراتبی دوسطحی پیشنهادی استفاده شده است. از بسته‌ی نرم­افزاری Opt Quest برای حل مسئله‌ی بهینه­ سازی در سطح بالای چارچوب پیشنهادی به‌منظور تهیه‌ی یک برنامه‌ی بلندمدت بهینه‌ی تولیدی استفاده شده است. در سطح پایین چارچوب، به کمک رویکرد شبه مدل‌‌محور یک تابع قطعی جهت برآورد دقیق میزان ماشین‌آلات بارگذاری با تعیین مقدار بهینه‌ی بیل­های مکانیکی با استفاده از طراحی آزمایش­ها، یک برنامه‌ی کوتاه‌مدت متناسب‌سازی شده است. محاسبات انجام‌شده در مجتمع معدنی مس سرچشمه نشان می­دهد که چگونه چارچوب پیشنهادی قادر است در میزان تولیدها، هزینه­های جابجایی مواد، و درآمد، اثر مطلوب بگذارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analytical Based Simulation Modeling for Short-Term and Long-Term Planning in Open Pit Mining

نویسندگان [English]

  • Milad Abolghasemian 1
  • Maryam Daneshmand-Mehr 2
  • Erfan Hassannayebi 3
1 Department of Industrial Engineering, Ayandegan University, Tonekabon, Iran.
2 Department of Industrial engineering, Branch of lahijan, Islamic Azad University, Lahijan, Iran
3 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
چکیده [English]

Open-pit mining operations are complex and multifaceted, requiring meticulous planning to optimize resource extraction, operational efficiency, and cost-effectiveness. In this paper, a two-level hierarchical framework based on simulation is presented for production planning and material loading machines in Iran's largest open-pit copper mine. Analytical-based simulation modeling offers a powerful tool for mining engineers and planners to simulate various scenarios, assess the impact of different decisions, and optimize mining operations. By integrating mathematical models, statistical analysis, and simulation techniques, this approach enables the evaluation of alternative strategies, risk assessment, and performance forecasting in open-pit mining environments. This paper aims to explore the application of analytical-based simulation modeling in the context of short-term and long-term planning in open-pit mining. It will delve into the methodology, tools, and techniques employed in developing simulation models for optimizing operational processes, enhancing productivity, and mitigating risks in open-pit mining operations. The study will also highlight the benefits, challenges, and future prospects of utilizing analytical-based simulation modeling for effective planning and decision-making in the dynamic field of open-pit mining. To solve the problem of planning machinery in mining, a planning problem has been formulated and applied in the framework of the proposed two-level hierarchical method. The Opt Quest software package to solve the optimization problem at the high level of the proposed framework in order to prepare a long-term optimal production plan. At the lower level of the framework, with the help of a meta-modeling approach, a deterministic function has been fitted to accurately estimate the number of loading machines by determining the optimal number of shovels using the design of experiments. The calculations performed in this mining complex show how the proposed framework can have a favorable effect on the amount of production, material handling costs and income. By embracing analytical-based simulation modeling, mining managers can make informed decisions, mitigate risks, and drive operational excellence in open-pit mining environments. This approach empowers managers to proactively address challenges, capitalize on opportunities, and steer mining operations towards greater efficiency, profitability, and sustainability in both the short and long term.

کلیدواژه‌ها [English]

  • Hierarchical framework
  • simulation based on optimization
  • long-term planning
  • short-term planning
  • design of experiment

مراجع

1- Abolghasemian, M., Kanai, A. G., and Daneshmandmehr, M. 2020. A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies, 13(4), pp. 705-732. https://doi.org/10.22059/ijms.2020.294809.673898
2- Nageshwaraniyer, S. S., Son, Y. J., and Dessureault, S. 2013. Simulation-based optimal planning for material handling networks in mining. Simulation, 89(3), pp. 330-345. https://doi.org/10.1177/0037549712464278
3- de Sousa Junior, W.T., Montevechi, J.A.B., de Carvalho Miranda, R. and Campos, A.T., 2019. Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review. Computers & Industrial Engineering, 128, pp.526-540. https://doi.org/10.1016/j.cie.2018.12.073
4- Abolghasemian, M., Kanafi, A. G., and Daneshmand-Mehr, M. 2022. Simulation-Based Multiobjective Optimization of Open-Pit Mine Haulage System: A Modified-NBI Method and Meta Modeling Approach. Complexity, 2022. https://doi.org/10.1155/2022/3540736
5- Jahangiri, S., Abolghasemian, M., Ghasemi, P., and Chobar, A. P. 2023. Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 43(1), pp. 1-19. https://doi.org/10.1504/IJISE.2023.128399
6- Chang, K.H., Chen, T.L., Yang, F.H. and Chang, T.Y., 2023. Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident. European Journal of Operational Research, 309(3), pp.1237-1262. https://doi.org/10.1016/j.ejor.2023.01.065
7-Ghasemi, A., Farajzadeh, F., Heavey, C., Fowler, J. and Papadopoulos, C.T., 2024. Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap. Journal of Industrial Information Integration, 39 p.100599. https://doi.org/10.1016/j.jii.2024.100599
8- Tekin, E., and Sabuncuoglu, I. 2004. Simulation optimization: A comprehensive review on theory and applications. IIE transactions, 36(11), pp. 1067-1081. https://doi.org/10.1080/07408170490500654
9- Teck, S., Dewil, R. and Vansteenwegen, P., 2024. A simulation-based genetic algorithm for a semi-automated warehouse scheduling problem with processing time variability. Applied Soft Computing, 160, pp.111713. https://doi.org/10.1016/j.asoc.2024.111713
10- Jafferali, M., Venkateshwaran, J., and Son, Y. J. 2005. Performance comparison of search-based simulation optimisation algorithms for operations scheduling. International Journal of Simulation and Process Modelling, 1(1-2), pp. 58-71. https://doi.org/10.1504/IJSPM.2005.007114
11- Ozdemir, B. and Kumral, M., 2019. Simulation-based optimization of truck-shovel material handling systems in multi-pit surface mines. Simulation Modelling Practice and Theory, 95, pp.36-48. https://doi.org/10.1016/j.simpat.2019.04.006
12- Zhang, L., and Xia, X. 2015. An integer programming approach for truck-shovel dispatching problem in open-pit mines. Energy Procedia, 75, pp. 1779-1784. https://doi.org/10.1016/j.egypro.2015.07.469
13- Chang, K.H. and Lin, C.P., 2023. An efficient simulation optimization method for the redundancy allocation problem with a chance constraint. Journal of the Operational Research Society, pp.1-15. https://doi.org/10.1080/01605682.2023.2272860
14- Shishvan, M.S. and Benndorf, J., 2019. Simulation-based optimization approach for material dispatching in continuous mining systems. European Journal of Operational Research, 275(3), pp.1108-1125. https://doi.org/10.1016/j.ejor.2018.12.015
15- Koenigsberg, E. 1982. Twenty five years of cyclic queues and closed queue networks: A review. Journal of the Operational Research Society, 33(7), pp. 605-619. https://doi.org/10.1057/jors.1982.136
16- Miranda, R.D.C., Montevechi, J.A.B., Silva, A.F.D. and Marins, F.A.S., 2014. A new approach to reducing search space and increasing efficiency in simulation optimization problems via the fuzzy-DEA-BCC. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/450367
17- Patel, M., and Desai, D. A. (2018). Critical review and analysis of measuring the success of Six Sigma implementation in manufacturing sector. International Journal of Quality & Reliability Management, 35(8), 1519-1545. https://doi.org/10.1108/IJQRM-04-2017-0081
18- Chinbat, U. and Takakuwa, S., 2009, December. Using simulation analysis for mining project risk management. In Proceedings of the 2009 Winter Simulation Conference (WSC), (pp. 2612-2623). IEEE. https://doi.org/10.1109/WSC.2009.5429647
19- Sbarbaro, H.M.D., 2021. Simulation Of Haulage Systems Using A Flexible Simulation Environment. IFAC-PapersOnLine, 54(11), pp.133-138. https://doi.org/10.1016/j.ifacol.2021.10.063
20- Alarie, S., and Gamache, M. 2002. Overview of solution strategies used in truck dispatching systems for open pit mines. International Journal of Surface Mining, Reclamation and Environment, 16(1), pp. 59-76. https://doi.org/10.1076/ijsm.16.1.59.3408
21- Sgurev, V., Vassilev, V., Dokev, N., Genova, K., Drangajov, S., Korsemov, C., and Atanassov, A. 1989. TRASY—an automated system for real-time control of the industrial truck haulage in open-pit mines. European Journal of Operational Research, 43(1), pp. 44-52. https://doi.org/10.1016/0377-2217(89)90408-6
22- Chanda, E. K. C., and Dagdelen, K. 1995. Optimal blending of mine production using goal programming and interactive graphics systems. International Journal of Surface Mining and Reclamation, 9(4), pp. 203-208. https://doi.org/10.1080/09208119508964748
23- Shih, J. S., and Frey, H. C. 1995. Coal blending optimization under uncertainty. European Journal of Operational Research, 83(3), pp. 452-465. https://doi.org/10.1016/0377-2217(94)00243-6
24- Runciman, N., Vagenas, N., and Corkal, T. 1997. Simulation of haulage truck loading techniques in an underground mine using WITNESS. Simulation, 68(5), pp. 291-299. https://doi.org/10.1177/003754979706800504
25- Chinbat, U., and Takakuwa, S. 2008. Using operation process simulation for a six sigma project of mining and iron production factory. Winter Simulation Conference, pp. 2431-2438. https://doi.org/10.1109/WSC.2008.4736351
26- Mena, R., Zio, E., Kristjanpoller, F., and Arata, A. 2013. Availability-based simulation and optimization modeling framework for open-pit mine truck allocation under dynamic constraints. International Journal of mining science and Technology, 23(1), pp. 113-119. https://doi.org/10.1016/j.ijmst.2013.01.017
27- Taghizadeh, H., Bazrkar, A., & Abedzadeh, M. 2015. Optimization production planning using fuzzy goal programming techniques. Modern Applied Science, 9(9), pp. 68. http://dx.doi.org/mas.v9n9p68
28- Saidabad, A. A., & Taghizadeh, H. 2015. Performance and improvement of production line function using computer simulation (case study: An iron foundry). American Journal of Computational Mathematics, 5(4), pp. 431. https://doi.org/10.4236/ajcm.2015.54038
 

فایل ها

هم رسانی

ارجاع به این مقاله

آمار