436
3 CONCLUSION
The traditional phase design concept utilizing pit parametrization is widely used in the mining industry. The case study provided in this paper shows that the schedules based on traditional mine phase design could miss a significant number of potential ounces especially in
the earlier stages of production that may result in poor cash flows. The underlying reason
is mainly the negligence of the operational requirements such as multi capacity, multi destinations, blending requirements, truck hours, and stockpiles together with the time value
of money. Given the pre-determined phase shapes, bench by bench scheduling limits the
flexibility of the production, therefore block by block scheduling will always generate the
most profitable schedules. However, to increase the practicality of the mine plans, the block
by block schedules can be grouped into phases for the pit design with haul roads and these
phases can be scheduled bench by bench. The novel integer solution algorithm developed at
CSM can generate block by block schedules to the mine planning problems modeled with
multi capacities, grade blending, grade uncertainty, stockpiles, variable pit slopes, multi destinations and truck hours (Aras, 2018). It should be emphasized that the blocks will not have
any pre-determined destinations based on grades, cycle times, material type or some other
criteria since the best destination selection per block will be done automatically during the
optimization process to maximize the NPV, in other words the dynamic cutoff concept is
employed. Another important factor is that the guided phase designs will reflect the risk
tolerance of the operator since the underlying block by block schedules already unlocked the
regions with the minimal risk exposure in the earlier stages of the production. Therefore, the
block by block schedules based on an optimal integer solution will guide the phases towards
generating the highest achievable profits both in the earlier stages of the production and
the life of mine while honoring the operational and practical requirements. Although not
illustrated in the case study, stockpiling the ore material can be easily included in the model.
REFERENCES
Aras, C. (2018). A new integer solution algorithm to solve open-pit mine production scheduling problems. Ph.D. Thesis, Colorado School of Mines, Golden, CO.
Bienstock, D., and Zuckerberg, M. (2009). A new LP algorithm for precedence constrained production
scheduling. Industrial Engineering, 1–33.
Bienstock, D., and Zuckerberg, M. (2010). Solving LP relaxations of large-scale precedence constrained
problems. Lecture Notes in Computer Science, 1–14.
Bienstock, D., and Zuckerberg, M. (2015). A new LP algorithm for precedence constrained production
scheduling. Industrial Engineering, 1–53.
Dagdelen, K., and Francois-Bongarcon D. (1982). Towards the complete double parameterization of
recovered reserves in open pit mining. 17th APCOM, 288–296.
Gershon, M. (1987). Heuristic approaches for mine planning and production scheduling. International
Journal of Mining and Geological Engineering, 5(1), 1–13.
Johnson, T.B. (1968). Optimum open pit mine production scheduling. Ph.D. Thesis, University of California, Berkeley, CA.
Meagher, C., and Dimitrakopoulos, R., and Avis, D. (2014). Optimized open pit mine design, pushbacks
and the gap problem-a review. Journal of Mining Science, 50(3), 508–526.
Ramazan, S. and Dagdelen, K. (1998). A new push back design algorithm in open pit mining. Mine
Planning and Equipment Selection, 119–124.
Seymour. F. (1995). Pit limit parametrization from modified 3D Lerchs—Grossmann algorithm. Society for Mining, Metallurgy and Exploration, 95–96.
Somrit, C. (2011). Development of a new open pit mine phase design algorithm using mixed integer
linear programming. Ph.D. Thesis, Colorado School of Mines, Golden, CO.
Whittle, J. (1998). Beyond optimization in open pit design. Proceedings of the First Canadian Conference on Computer Applications in the Mineral Industry, 331–337.
3 CONCLUSION
The traditional phase design concept utilizing pit parametrization is widely used in the mining industry. The case study provided in this paper shows that the schedules based on traditional mine phase design could miss a significant number of potential ounces especially in
the earlier stages of production that may result in poor cash flows. The underlying reason
is mainly the negligence of the operational requirements such as multi capacity, multi destinations, blending requirements, truck hours, and stockpiles together with the time value
of money. Given the pre-determined phase shapes, bench by bench scheduling limits the
flexibility of the production, therefore block by block scheduling will always generate the
most profitable schedules. However, to increase the practicality of the mine plans, the block
by block schedules can be grouped into phases for the pit design with haul roads and these
phases can be scheduled bench by bench. The novel integer solution algorithm developed at
CSM can generate block by block schedules to the mine planning problems modeled with
multi capacities, grade blending, grade uncertainty, stockpiles, variable pit slopes, multi destinations and truck hours (Aras, 2018). It should be emphasized that the blocks will not have
any pre-determined destinations based on grades, cycle times, material type or some other
criteria since the best destination selection per block will be done automatically during the
optimization process to maximize the NPV, in other words the dynamic cutoff concept is
employed. Another important factor is that the guided phase designs will reflect the risk
tolerance of the operator since the underlying block by block schedules already unlocked the
regions with the minimal risk exposure in the earlier stages of the production. Therefore, the
block by block schedules based on an optimal integer solution will guide the phases towards
generating the highest achievable profits both in the earlier stages of the production and
the life of mine while honoring the operational and practical requirements. Although not
illustrated in the case study, stockpiling the ore material can be easily included in the model.
REFERENCES
Aras, C. (2018). A new integer solution algorithm to solve open-pit mine production scheduling problems. Ph.D. Thesis, Colorado School of Mines, Golden, CO.
Bienstock, D., and Zuckerberg, M. (2009). A new LP algorithm for precedence constrained production
scheduling. Industrial Engineering, 1–33.
Bienstock, D., and Zuckerberg, M. (2010). Solving LP relaxations of large-scale precedence constrained
problems. Lecture Notes in Computer Science, 1–14.
Bienstock, D., and Zuckerberg, M. (2015). A new LP algorithm for precedence constrained production
scheduling. Industrial Engineering, 1–53.
Dagdelen, K., and Francois-Bongarcon D. (1982). Towards the complete double parameterization of
recovered reserves in open pit mining. 17th APCOM, 288–296.
Gershon, M. (1987). Heuristic approaches for mine planning and production scheduling. International
Journal of Mining and Geological Engineering, 5(1), 1–13.
Johnson, T.B. (1968). Optimum open pit mine production scheduling. Ph.D. Thesis, University of California, Berkeley, CA.
Meagher, C., and Dimitrakopoulos, R., and Avis, D. (2014). Optimized open pit mine design, pushbacks
and the gap problem-a review. Journal of Mining Science, 50(3), 508–526.
Ramazan, S. and Dagdelen, K. (1998). A new push back design algorithm in open pit mining. Mine
Planning and Equipment Selection, 119–124.
Seymour. F. (1995). Pit limit parametrization from modified 3D Lerchs—Grossmann algorithm. Society for Mining, Metallurgy and Exploration, 95–96.
Somrit, C. (2011). Development of a new open pit mine phase design algorithm using mixed integer
linear programming. Ph.D. Thesis, Colorado School of Mines, Golden, CO.
Whittle, J. (1998). Beyond optimization in open pit design. Proceedings of the First Canadian Conference on Computer Applications in the Mineral Industry, 331–337.