Evaluating the Effect of Fleet Management on the Performance of Mining Operations Using Integer Linear Programming Approach and Two Different Strategies

Document Type : Research Paper

Authors

1 Assistant Professor, Sahand University of Technology, Tabriz, Iran

2 PhD Student, Sahand University of Technology, Tabriz, Iran

3 MSc, Sahand University of Technology, Tabriz, Iran

Abstract

This paper presents an integer linear programming (ILP) model for allocating trucks according to their operating performances in a truck-shovel system of an open-pit mine, so as to minimize the total operating cost of trucks. To evaluate the performance of the proposed model, the model was applied with two different strategies, namely independent fleet management and integrated fleet management. The results of the research in a copper mine case study showed that the developed strategies were capable of handling the operation with a fewer number of trucks than the actual mine strategy. In addition, integrated fleet management indicated 2% and 3% cost-savings over the shift compared to strategy 1 and the mine allocation schedule, respectively.

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References

Alarie, S., & Gamache, M. (2002). Overview of solution strategies used in truck dispatching systems for open-pit mines. International Journal of Mining, Reclamation and Environment, 16(1), 59–76.
Bajany, D., Xia, X., Zhang, L., & Milp, A. (2017).  Model for truck-shovel scheduling to minimize fuel consumption. Energy Procedia,  , 2739–2745.
Bakhtavar, E., & Mahmoudi, H. (2020). Development of a scenario-based robust model for the optimal truck-shovel allocation in open-pit mining. Computers & Operations Research,  , 104539.
Barnes, R. J., King, M. S., & Johnson, T. B. (1978). Probability techniques for analyzing open pit production systems [paper presentation]. 16th APCOM, University of Arizona, Tucson, AZ.
Both, C., & Dimitrakopoulos, R. (2020). Joint stochastic short-term production scheduling and fleet management optimization for mining complexes. Optimization and Engineering, 21(4), 1–27.
Carmichael, D. G. (1987). Engineering queues in construction and mining. Ellis Horwood Ltd.
Chang, Y., Ren, H., & Wang, S. (2015). Modelling and optimizing an open-pit truck scheduling problem. Discrete Dynamics in Nature and Society, 2015, 1–8.
Dallaire, R., Lapante, A. R., & Elbrond, J. (1978). Humphrey’s spiral tolerance to feed variations. CIM Bull, 71(796), 128–134.
de Melo, W. B. (2021). Optimization of truck allocation in open pit mines using differential evolution algorithm. International Journal of Innovation and Research, 9(8), 338-350.
Eivazy, H., & Askari-Nasab, H. (2012). A mixed integer linear programming model for short-term open pit mine production scheduling. International Journal of Mining Technology, 121(2), 97–108.
Ercelebi, S. G., & Bascetin, A. (2009). Optimization of shovel-truck system for surface mining. Journal of The South African Institute of Mining and Metallurgy, 109(7), 433-439.
Gamache, M., & Desaulniers, G., & Hébert-Desgroseilliers, L. (2009). A generic linear program for an optimal mine production plan. Groupe d’études et de recherche en analyse des décisions.
Goodfellow, R. C., & Dimitrakopoulos, R. (2016). Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing,  , 292–304.
Govinda Raj, M., Vardhan, H., & Rao, Y. (2009). Production optimisation using simulation models in mines: A critical review. International Journal of Operational Research, 6(3), 330-359.
Kappas G., & Yegulalp, T. M. (1991). An application of closed queueing networks theory in truck-shovel systems. International Journal of Surface Mining, Reclamation and Environment, 5(1), 45–51.
Koenigsberg, E. (1958). Cyclic queues. Journal of the Operational Research Society, 9(1), 22–35.
L’Heureux, G., Gamache, M., & Soumis, F. (2013). Mixed integer programming model for short term planning in open-pit mines. International Journal of Mining Technology, 122(2), 101–109.
Li, Z. (1990). A methodology for the optimum control of shovel and truck operations in open-pit mining. International Journal of Mining Science and Technology, 10(3), 337–340.
Liu, G., Chai, S. (2019). Optimizing open-pit truck route based on minimization of time-varying transport energy consumption. Mathematical Problems in Engineering, 2019, 1-12.
Manríquez, F., González, H., & Morales, N. (2019). Short-term open-pit mine production scheduling with hierarchical objectives [paper presentation]. Mining goes Digital: Proceedings of the 39th International Symposium’Application of Computers and Operations Research in the Mineral Industry’(APCOM 2019), June 4-6, 2019, Wroclaw, Poland. CRC Press.
Matamoros, M. E. V., & Dimitrakopoulos, R. (2016). Stochastic short-term mine production schedule accounting for fleet allocation: Operational considerations and blending restrictions. International Journal of Operational Research, 255(3), 911–921.
Micholopulos T., & Panagiotou, G. (2001). Truck allocation using stochastic goal programming. Proceeding of Mine Planning & Equipment Selection, New Delhi, India, pp. 965–970.
Mirzaei-Nasirabad, H., Mohtasham, M., & and Omidbad, M. (2019). Comparison of the various dispatching strategies for truck-shovel productivity optimization in open pit mines. International Journal of Mining Technology, 53(2), 193–201.
Mohtasham, M., Mirzaei-Nasirabad, H., & Alizadeh, B. (2021). Optimization of truck-shovel allocation in open-pit mines under uncertainty: A chance-constrained goal programming approach. International Journal of Mining Technology, 130(2), 81-100.
Mohtasham, M., Mirzaei-Nasirabad, H., Askari-Nasab, H., & Alizadeh, B. (2021). A multi-objective model for fleet allocation schedule in open-pit mines considering the impact of prioritising objectives on transportation system performance. International Journal of Mining, Reclamation and Environment, 35(9), 1-19.
Mohtasham, M., Mirzaei-Nasirabad, H., & Mahmoodi Markid, A. (2017). Development of a goal programming model for optimization of truck allocation in open pit mines. International Journal of Mining Technology, 8(3), 359–371.
Rubito, E. (2007). Mill feed optimization for multiple processing facilities using integer linear programming [paper presentation]. The Proceedings of the fifteen-international symposium on mine planning and equipment selection, Turin, Italy.
Shah, K., & Rehman, S. Ur. (2020). Modeling and optimization of truck-shovel allocation to mining faces in cement quarry, Journal of Mining and Environment, 11(1), 21–30.
Soumis, F., Ethier, F., & Elbrond, J. (1989). Evaluation of the new truck dispatching in the Mount Wright Mine. In 21st APCOM, Littleton, CO, pp. 674–682.
Ta, C. H., Kresta, J. V., Forbes, J. F., & Marquez, H. J. (2005). A stochastic optimization approach to mine truck allocation. International Journal of Mining, Reclamation and Environment, 19(3), 162–175.
Temeng, V. A., Otuonye, F. O., & Frendewey, J. O. (1998). A non-preemptive goal programming approach to truck dispatching in open pit mines. Mineral Resources Engineering, 7(2), 59-67.
Topal, E., & Ramazan, S. (2010). A new MIP model for mine equipment scheduling by minimizing maintenance cost. European Journal of Operational Research, 207(2), 1065-1071.
Torkamani E., & Askari-Nasab, H. (2015). A linkage of truck-and-shovel operations to short-term mine plans using discrete-event simulation. International Journal of Mining and Mineral Engineering, 6(2), 97–118.
Upadhyay, S. P., & Askari-Nasab, H. (2016). Truck-shovel allocation optimisation: A goal programming approach. International Journal of Mining Technology, 125(2), 1–11.
White J. W., & Olson, J. P. (1986). Computer-based dispatching in mines with concurrent operating objectives. Mining Engineering, Littleton, 38(11), 1045–1054.
Xi, Y., & Yegulalp, T. M. (1993). Optimum dispatching algorithm for Anshan open-pit mine [paper presentation]. 24th APCOM, Canadian
Institute of Mining, Metallurgy and Petroleum, Montreal, Quebec, Canada.
Zhang, Y., Li, S., & Cai, Q. (1990). Optimization criteria for computer-controlled truck dispatching system [paper presentation]. 22nd Application of Computers and Operations Research in the Minerals Industry, Berlin, Germany.
 
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 16, Issue 1
January 2023
Pages 139-155

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