توسعه الگوریتمی جستجو محور برای حل مسئله تخصیص و گسیل ناوگان حمل و نقل در معادن روباز

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی استخراج معدن، دانشکده فنی و مهندسی، دانشگاه تربیت مدرس، تهران

2 استاد، گروه مهندسی استخراج معدن، دانشکده فنی و مهندسی، دانشگاه تربیت مدرس، تهران

3 استاد، گروه عمران و محیط زیست، دانشکده فنی و مهندسی، دانشگاه آلبرتا، ادمونتون، کانادا

4 استادیار، گروه سیستم‌های اقتصادی و اجتماعی، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران

5 استادیار، گروه مهندسی استخراج معدن، دانشکده فنی و مهندسی، دانشگاه تربیت مدرس، تهران

چکیده

عملیات بارگیری و باربری در معادن روباز، به ‌عنوان آخرین مرحله‌ فرآیند استخراج در نظر گرفته می‌شود. برای انجام این عملیات، استفاده از سیستم شاول- کامیون به دلیل مزایای زیاد مانند انعطاف‌پذیری بالا، ارجحیت دارد. به‌ دلیل هزینه‌های عملیاتی زیاد، مدیریت مناسب ناوگان و بهینه‌سازی در این بخش به ‌طور قابل توجهی در اقتصاد پروژه موثر است. مساله تخصیص و گسیل کامیون، به ‌ویژه در معادن بزرگ با نقاط بارگیری و تخلیه متعدد بسیار پیچیده است. با توجه به اندازه و پیچیدگی مساله، استفاده از روش‌های حل ریاضی به ‌دلیل زمان حل بسیار زیاد که به استفاده از ابررایانه‌ها منجر می‌شود، توجیه‌پذیر نیست. برای رفع این کاستی‌ها می‌توان از الگوریتم‌های ابتکاری استفاده کرد. در این مقاله، یک الگوریتم ابتکاری در محیط نرم‌افزار MATLAB، برای حل مساله تخصیص و گسیل یک معدن واقعی توسعه داده شده است. با توجه به نتایج به‌ دست آمده، زمان اجرای الگوریتم ابتکاری 39 ثانیه محاسبه شده است. در نهایت حل همین مساله با یک مدل ریاضی موجود طی 24 ساعت، نشان‌دهنده برتری الگوریتم پیشنهادی نسبت به مدل‌سازی ریاضی است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Developing a Local Search Algorithm for Solving the Allocation and Dispatching Problem of Transportation Fleet in Open Pit Mines

نویسندگان [English]

  • H. Pirmoradian 1
  • M. Monjezi 2
  • H. Askari-Nasab 3
  • E. Nikbakhsh 4
  • A. Mousavi Nogholi 5
1 Ph.D Student, Dept. of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
2 Professor, Dept. of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
3 Professor, Dept. of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Edmonton, Canada
4 Assistant Professor, Dept. of Economic and Social Systems, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
5 Assistant Professor, Dept. of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

Loading and haulage operation in open pit mines is the last stage of the mining process. truck- shovel system, due to its many advantages  including high flexibility, is preferred for this operation. Due to high operating costs, proper fleet management and optimization can significantly affect the project economics. Truck allocation and dispatching issue is a very complex problem, especially in large mines with numerous loading and dumping points. Because of the problem size and complexity, employing mathematical methods is not justified due to very high solution time which leads to employing super computers. To overcome the aforesaid shortcoming, heuristic algorithms can be applied. In this paper, in MATLAB environment, a heuristic algorithm was developed to solve allocation and dispatching problem of transportation fleet of a real mine. According to the obtained results, a running time of 39 seconds was computed for the heuristic algorithm. Finally, the same problem was solved with an available mathematical model with a running time of 24 hours which shows the superiority of the proposed algorithm over the mathematical modeling.

کلیدواژه‌ها [English]

  • Loading and haulage operation
  • Allocation and dispatching problem
  • Heuristic algorithm

مراجع

  1. Bajany, D. M., Xia, X., and Zhang, L. (2017). “A MILP Model for Truck-Shovel Scheduling to Minimize Fuel Consumption”. The 8th International Conference on Applied Energy, 125: 2739- 2745.
  2. Isnafitri, M. F., Rosyidi, C., and Aisyati A. (2021). “A Truck Allocation Optimization Model in Open Pit Mining to Minimize Investment and Transportation Costs”. In: IOP Conference Series: Materials Science and Engineering, 1096(1): 12-24.
  3. Shah, K. S., and Rehman, S. U. (2020). “Modeling and Optimization of truck-shovel allocation to mining faces in cement quarry”. Journal of Mining Environment, 11(1): 21-30.
  4. Ghobadi-Samani, M., Monjezi, M., Khademi Hamidi, J., and Mousavinogholi, A. (2020). “A Mathematical Model to Optimize Allocation Sequence in Dispatching Problem”. Journal of Mining and Environment, 11(1): 185-192.
  5. Kaveh Ahangaran, D., Yasrebi, A. B., Wetherelt, A., and Foster, P. (2012). “Real–time dispatching modelling for trucks with different capacities in open pit mines”. Archives of Mining Sciences, 57(1): 39-52.
  6. Moradi-Afrapoli, A. (2018). “A Hybrid Simulation and Optimization Approach towards Truck Dispatching Problem in Surface Mines”. PhD. Thesis, University of Alberta, Department of Civil and Environmental Engineering. https://era.library.ualberta.ca.
  7. صائبی‌نیا، ر.، موسوی، الف.، صیادی، الف.؛ 1401؛ "ارائه یک مدل ریاضی یکپارچه برای بهینه‌سازی تخصیص و گسیل کامیون‌ها در معادن روباز". نشریه روش‌های تحلیلی و عددی در مهندسی معدن، دوره 12، شماره 30، ص 101-91.
  8. Pinedo, M., and Hadavi, K. (1991). “Scheduling: theory, algorithms and systems development”. In: Gaul, W., Bachem, A., Habenicht, W., Runge, W., and Stahl, W. W. (Eds.), Operations Research Proceedings, Springer, Berlin, Heidelberg, 1991: 35-42. DOI: https://doi.org/10.1007/978-3-642-46773-8_5.
  9. 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): 59-76.
  10. Moradi-Afrapoli, A., and 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.
  11. Zeng, W. (2018). “A simulation model for truck-shovel operation”. PhD. thesis, University of Wollongong, School of Civil, Mining and Environmental Engineering, March. https://ro.uow.edu.au/theses1/270.
  12. Ataeepour, M., and Baafi E. Y. (1999). “Arena simulation model for truck-shovel operation in despatching and non-despatching modes”. International Journal of Surface Mining, Reclamation and Environment, 13(3): 125-129.
  13. He, M. X., Wei, J. C., Lu, X. M., and Huang, B. X. (2010). “The genetic algorithm for truck dispatching problems in surface mine”. Information Technology Journal, 9(4): 710-714.
  14. Saadatmand-Hashemi, A., and Sattarvand, J. (2015). “Simulation Based Investigation of Different Fleet Management Paradigms in Open Pit Mines-A Case Study of Sungun Copper Mine”. Archives of Mining Sciences, 60(1): 195-208.
  15. Upadhyay, S. P., and Askari-Nasab, H. (2016). “Truck-shovel allocation optimization: a goal programming approach”. Mining Technology, 125(2): 1-11.
  16. Mohtasham, M., Mirzaei Nasirabad, H., and Mahmoodi Markid, A. (2017). “Development of a goal programming model for optimization of truck allocation in open pit mines”. Journal of Mining and Environment, 8(3): 359-371.
  17. Chaowasakoo, P., Seppala, H., Koivo, H., and Zhou, Q. (2017). “Digitalization of mine operations: Scenarios to benefit in real-time truck dispatching”. International Journal of Mining Science and Technology, 27: 229-236.
  18. Chaowasakoo, P., Seppala, H., Koivo, H., and Zhou, Q. (2017). “Improving fleet management in mines: The benefit of heterogeneous match factor”. European Journal of Operational Research, 261(3): 1052-1065. DOI: https://doi.org/10.1016/j.ejor.2017.02.039.
  19. Hauck, R. F. (1973). “A Real-Time Dispatching Algorithm for Maximizing Open-Pit Mine Production under Processing and Blending Requirements”. In: Seminar on Scheduling in Mining, Smelting and Metallurgy, 1-10.
  20. Soumis, F., Ethier, J., and Elbrond, J. (1989). “Evaluation of the New Truck Dispatching in the Mount Wright Mine”. In: Twenty-First Applications for Computers and Operations Research in the Minerals Industries (APCOM), 674-682.
  21. Li, Z. (1990). “A methodology for the optimum control of shovel and truck operations in open-pit mining”. Mining Science and Technology, 10(3): 337-340.
  22. Topal, E., and Ramazan, S. (2010). “A new MIP model for mine equipment scheduling by minimizing maintenance cost”. European Journal of Operational Research, 207(2): 1065-1071.
  23. Topal, E., and Ramazan, S. (2012). “Mining truck scheduling with stochastic maintenance cost”. Journal of Coal Science and Engineering, 18(3): 313-319.
  24. Kaveh-Ahangaran, D., Yasrebi, A. B., Wetherelt, A., and Foster, P. (2012). “Real–time dispatching modelling for trucks with different capacities in open pit mines”. Archives of Mining Sciences, 57(1): 39-52.
  25. Chang, Y., Ren, H., and Wang, S. (2015). “Modelling and optimizing an open-pit truck scheduling problem”. Discrete Dynamics in Nature and Society, 2015: 1-7.
  26. Zhang, L., and Xia, X. (2015). “An integer programming approach for truck-shovel dispatching problem in open-pit mines”. In: Energy Procedia, 75: 1779-1784.
  27. Panagiotou, G., and Michalakopoulos, T. (2001). “A computer-based truck dispatching system for small-medium scale mining operations”.
    International Journal of Surface Mining, Reclamation and Environment, 8(1): 1-15.
  28. Temeng, V. A., Otuonye, F. O., and Frendewey, J. O. (1997). “Real-time truck dispatching using a transportation algorithm”. International Journal of Surface Mining, Reclamation and Environment, 11(4): 203-207.
  29. Ta, C. H., Kresta, J. V., Forbes, J. F., and Marquez, H. J. (2005). “A stochastic optimization approach to mine truck allocation”. International Journal of Surface Mining, Reclamation and Environment, 19(3): 162-175.
  30. Subtil, R. F., Silva, D. M., and Alves, J. C. (2011). “A practical approach to truck dispatch for open pit mines”. In: Thirty-Fifth Applications for Computers and Operations Research in the Minerals Industries (APCOM) Symposium, 765-777.
  31. Moradi-Afrapoli, A., Tabesh, M., and Askari-Nasab, H. (2018). “A stochastic hybrid simulation-optimization approach towards haul fleet sizing in surface mines”. Mining Technology, 128(1): 1-12.
  32. Moradi-Afrapoli, A., Tabesh, M., and Askari-Nasab, H. (2019). “A multiple objective transportation problem approach to dynamic truck dispatching in surface mines”. European Journal of Operational Research, 276(1): 331-342.
  33. Moradi-Afrapoli, A., Upadhyay, S., and Askari-Nasab, H. (2021). “Truck dispatching in surface mines Application of fuzzy linear programming”. The Journal of the Southern African Institute of Mining and Metallurgy, 121(9): 505-512.
  34. Mohtasham, M., Mirzaei Nasirabad, H., and Alizadeh, B. (2021). “Optimization of truck-shovel allocation in openpit mines under uncertainty: a chance-constrained goal programming approach”. Journal of Mining Technology, 130(2): 81-100.
  35. Mirzaei-Nasirabad, H., Rahimzadeh-Nanekaran, F., and Mohtasham, M. (2023). “Evaluating the effect of fleet management on the performance of mining operations using integer linear programming approach and two different strategies”. Iranian Journal of Management Studies, 16(1): 139-155. DOI: https://doi.org/10.22059/ijms.2022.330990.674769.
  36. Mohtasham, M., Mirzaei Nasirabad, H., Askari-Nasab, H., and Alizadeh, B. (2022). “Multi-stage optimization framework for the real-time truck decision problem in open-pit mines: a case study on Sungun copper mine”. International Journal of Mining, Reclamation and Environment, 36(7): 461-491. DOI: 10.1080/17480930.2022.2067709.
  37. Gonzalez, T., and Sahni, S. (1978). “Flowshop and jobshop schedules: complexity and approximation”. Operation Research, 26(1): 36-52.
  38. Marichelvam, M. K., and Geetha, M. (2019). “A hybrid algorithm to solve the stochastic flow shop scheduling problems with machine break down”. International Journal of Enterprise Network Management, 10(2): 162-175.

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سابقه مقاله

  • تاریخ دریافت: 22 خرداد 1401
  • تاریخ بازنگری: 04 دی 1401
  • تاریخ پذیرش: 30 شهریور 1401

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