Abstract
Managing the flow of excavated materials from a mine pit and the subsequent processing steps is the logistical challenge in mining. Mine planning needs to consider various geometric and resource constraints while maximizing the net present value (NPV) of profits over a long horizon. This mine planning problem has been modelled and solved as a precedence constrained production scheduling problem (PCPSP) using heuristics, due to its NP-hardness. However, the recent push for sustainable and carbon-aware mining practices calls for new planning approaches. In this paper, we propose an efficient temporally decomposed greedy Lagrangian relaxation (TDGLR) approach to maximize profits while observing the stipulated carbon emission limit per year. With a collection of real-world-inspired mining datasets, we demonstrate how we generate approximated Pareto fronts for planners. Using this approach, they can choose mine plans that maximize profits while observing the given carbon emission target. The TDGLR was compared against a Mixed Integer Programming (MIP) model to solve a real mine dataset with the gaps not exceeding \(0.3178\%\) and averaging \(0.015\%\). For larger instances, MIP cannot even generate feasible solutions.
Supported by Enterprise Singapore.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Attari, M.Y.N., Torkayesh, A.E.: Developing benders decomposition algorithm for a green supply chain network of mine industry: case of Iranian mine industry. Oper. Res. Perspect. 5, 371–382 (2018)
Calas, G.: Mineral resources and sustainable development. Elem. Int. Mag. Mineral. Geochem. Petrol. 13(5), 301–306 (2017)
Canales-Bustos, L., Santibañez-González, E., Candia-Véjar, A.: A multi-objective optimization model for the design of an effective decarbonized supply chain in mining. Int. J. Prod. Econ. 193, 449–464 (2017)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_83
Espinoza, D., Goycoolea, M., Moreno, E., Newman, A.: MineLib: a library of open pit mining problems. Ann. Oper. Res. 206(1), 93–114 (2012). https://doi.org/10.1007/s10479-012-1258-3
Fisher, M.L.: The Lagrangian relaxation method for solving integer programming problems. Manage. Sci. 27(1), 1–18 (1981)
Fu, Z., Asad, M.W.A., Topal, E.: A new model for open-pit production and waste-dump scheduling. Eng. Optim. 51(4), 718–732 (2019)
Gorman, M.R., Dzombak, D.A.: A review of sustainable mining and resource management: transitioning from the life cycle of the mine to the life cycle of the mineral. Resour. Conserv. Recycl. 137, 281–291 (2018)
Hustrulid, W.A., Kuchta, M., Martin, R.K.: Open Pit Mine Planning and Design, Two Volume Set and CD-ROM Pack. CRC Press, Boca Raton (2013)
ICAP: Emissions trading worldwide: Status report 2022. ICAP Berlin (2022)
Johnson, D.S., Niemi, K.: On knapsacks, partitions, and a new dynamic programming technique for trees. Math. Oper. Res. 8(1), 1–14 (1983)
Johnson, T.B.: Optimum open pit mine production scheduling. California Univ Berkeley Operations Research Center, Technical report (1968)
Kenny, A., Li, X., Ernst, A.T., Thiruvady, D.: Towards solving large-scale precedence constrained production scheduling problems in mining. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1137–1144 (2017)
Levinson, Z., Dimitrakopoulos, R.: Simultaneous stochastic optimisation of an open-pit gold mining complex with waste management. Int. J. Min. Reclam. Environ. 34(6), 415–429 (2020)
Newman, A.M., Rubio, E., Caro, R., Weintraub, A., Eurek, K.: A review of operations research in mine planning. Interfaces 40(3), 222–245 (2010)
Reid, H., Denina, C.: Rio says climate change “at heart of strategy” after investors demand action, April 2022. https://www.reuters.com/business/energy/investors-urge-rio-tinto-cut-indirect-emissions-2022-04-08/
Rimélé, M.A., Dimitrakopoulos, R., Gamache, M.: A stochastic optimization method with in-pit waste and tailings disposal for open pit life-of-mine production planning. Resour. Policy 57, 112–121 (2018)
Valderrama, C.V., Santibanez-González, E., Pimentel, B., Candia-Vejar, A., Canales-Bustos, L.: Designing an environmental supply chain network in the mining industry to reduce carbon emissions. J. Clean. Prod. 254, 119688 (2020)
Wang, X., Gu, X., Liu, Z., Wang, Q., Xu, X., Zheng, M.: Production process optimization of metal mines considering economic benefit and resource efficiency using an NSGA-II model. Processes 6(11), 228 (2018)
Xu, X., Gu, X., Qing, W., Zhao, Y., Wang, Z.: Open pit limit optimization considering economic profit, ecological costs and social benefits. Trans. Nonferrous Met. Soc. Chin. 31(12), 3847–3861 (2021)
Xu, X., et al.: Production scheduling optimization considering ecological costs for open pit metal mines. J. Cleaner Prod. 180, 210–221 (2018)
Yağmur, E., Kesen, S.E.: Bi-objective optimization for joint production scheduling and distribution problem with sustainability. In: Mes, M., Lalla-Ruiz, E., Voß, S. (eds.) ICCL 2021. LNCS, vol. 13004, pp. 269–281. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87672-2_18
Zeng, L., Liu, S.Q., Kozan, E., Corry, P., Masoud, M.: A comprehensive interdisciplinary review of mine supply chain management. Resour. Policy 74, 102274 (2021)
Zhang, Z., Folmer, H.: The choice of policy instruments for the control of carbon dioxide emissions. Intereconomics 30(3), 133–142 (1995). https://doi.org/10.1007/BF02927268
Acknowledgements
This work is supported by Enterprise Singapore under the grant 20-IPPII-T-001-B-1 and Rio Tinto Ltd.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Azhar, N.A.B., Gunawan, A., Cheng, SF., Leonardi, E. (2022). A Carbon-Aware Planning Framework for Production Scheduling in Mining. In: de Armas, J., Ramalhinho, H., Voß, S. (eds) Computational Logistics. ICCL 2022. Lecture Notes in Computer Science, vol 13557. Springer, Cham. https://doi.org/10.1007/978-3-031-16579-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-16579-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16578-8
Online ISBN: 978-3-031-16579-5
eBook Packages: Computer ScienceComputer Science (R0)