9
planned and actual production, but other risk drivers
should be considered too.
The McLaughlin mine case study demonstrated the
stochastic evaluation process. Risk analysis identified time
periods with the highest uncertainty in expected results—
this can identify areas for additional drilling and/or time
periods where scaled-back forecasts may be appropriate.
The case study demonstrated how strategic cut-off grade
optimization and stockpiling improved mine plan value
significantly but did not create a significant reduction in
grade or financial uncertainty. In the case study, the value
impact of downside orebody uncertainty was comparable
to the value lost by not achieving planned vertical advance
or mining rates.
REFERENCES
Aras C., Dagdelen K., Johnson T., 2019. Generating push-
backs using direct block mine production scheduling
algorithm. In Mining Goes Digital, pp 426–436. Edited
by Mueller et al. London: Taylor &Francis Group.
Bowater, M., 2022. Crimes Against Mine Planning. Las
Vegas: Amazon.
Clark, L., Dagdelen, K. 2023. Practical Mine Planning and
Design. In SME Surface Mining Handbook. Edited by
P. Darling. Englewood, CO, USA: SME.
Goodfellow, R., Dimitrakopoulos, R., 2016. Global opti-
mization of open pit mining complexes with uncer-
tainty. Applied Soft Computing. 40: 292–304.
Hoerger S., 2024. MILP Production Scheduling Models
for Evaluating Continuous Improvement Projects. In
Minexchange 2024 SME Annual Conference, preprint.
Phoenix: SME.
Hoerger S., Dagdelen K., 2024. Conditional Simulation
for Stochastic Production Scheduling of Open Pit
Mines. In Geostats 2024 Conference Extended Abstracts.
Publication in progress: Springer.
Journel A., Huijbregts C., 1978. Mining Geostatistics, pp
444–490. London: Academic Press.
Lane, K., 1988. The Economic Definition of Ore—Cut-off
Grades in Theory and Practice. London: Mining Journal
Books.
Ortiz, J., 2020. Introduction to sequential Gaussian simu-
lation. In: Predictive Geometallurgy and Geostatistics
Lab Annual Report 2020, pp 7–19. Montreal: Queen’s
University.
Rendu, J.M., 2014. An Introduction to Cut-off Grade
Estimation, 2nd Edition. Englewood, CO, USA: SME.
Rossi, M., Deutsch C., 2014. Mineral Resource Estimation.
Heidelberg: Springer.
Wolsey, L., 2021. Integer Programming. 2nd Edition.
Hoboken: Wiley.
planned and actual production, but other risk drivers
should be considered too.
The McLaughlin mine case study demonstrated the
stochastic evaluation process. Risk analysis identified time
periods with the highest uncertainty in expected results—
this can identify areas for additional drilling and/or time
periods where scaled-back forecasts may be appropriate.
The case study demonstrated how strategic cut-off grade
optimization and stockpiling improved mine plan value
significantly but did not create a significant reduction in
grade or financial uncertainty. In the case study, the value
impact of downside orebody uncertainty was comparable
to the value lost by not achieving planned vertical advance
or mining rates.
REFERENCES
Aras C., Dagdelen K., Johnson T., 2019. Generating push-
backs using direct block mine production scheduling
algorithm. In Mining Goes Digital, pp 426–436. Edited
by Mueller et al. London: Taylor &Francis Group.
Bowater, M., 2022. Crimes Against Mine Planning. Las
Vegas: Amazon.
Clark, L., Dagdelen, K. 2023. Practical Mine Planning and
Design. In SME Surface Mining Handbook. Edited by
P. Darling. Englewood, CO, USA: SME.
Goodfellow, R., Dimitrakopoulos, R., 2016. Global opti-
mization of open pit mining complexes with uncer-
tainty. Applied Soft Computing. 40: 292–304.
Hoerger S., 2024. MILP Production Scheduling Models
for Evaluating Continuous Improvement Projects. In
Minexchange 2024 SME Annual Conference, preprint.
Phoenix: SME.
Hoerger S., Dagdelen K., 2024. Conditional Simulation
for Stochastic Production Scheduling of Open Pit
Mines. In Geostats 2024 Conference Extended Abstracts.
Publication in progress: Springer.
Journel A., Huijbregts C., 1978. Mining Geostatistics, pp
444–490. London: Academic Press.
Lane, K., 1988. The Economic Definition of Ore—Cut-off
Grades in Theory and Practice. London: Mining Journal
Books.
Ortiz, J., 2020. Introduction to sequential Gaussian simu-
lation. In: Predictive Geometallurgy and Geostatistics
Lab Annual Report 2020, pp 7–19. Montreal: Queen’s
University.
Rendu, J.M., 2014. An Introduction to Cut-off Grade
Estimation, 2nd Edition. Englewood, CO, USA: SME.
Rossi, M., Deutsch C., 2014. Mineral Resource Estimation.
Heidelberg: Springer.
Wolsey, L., 2021. Integer Programming. 2nd Edition.
Hoboken: Wiley.