Advanced mine optimisation under uncertainty using evolution

Author:

Reid William1,Neumann Aneta2,Ratcliffe Simon1,Neumann Frank2

Affiliation:

1. Maptek Pty. Ltd, Adelaide, Australia

2. The University of Adelaide, Adelaide, Australia

Funder

the South Australian Government through the Research Consortium Unlocking Complex Resources through Lean Processing

Publisher

ACM

Reference25 articles.

1. Evolutionary Computation for Real-World Problems

2. The Australasian Joint Ore Reserves Committee. 2012. The Australasian Code for Reporting of Exploration Results Mineral Resources and Ore Reserves. (2012). http://www.jorc.org/docs/JORC_code_2012.pdf The Australasian Joint Ore Reserves Committee. 2012. The Australasian Code for Reporting of Exploration Results Mineral Resources and Ore Reserves. (2012). http://www.jorc.org/docs/JORC_code_2012.pdf

3. Shubhabrata Datta and J. Paulo Davim (Eds.). 2019. Optimization in Industry Present Practices and Future Scopes. Springer. 10.1007/978-3-030-01641-8 Shubhabrata Datta and J. Paulo Davim (Eds.). 2019. Optimization in Industry Present Practices and Future Scopes. Springer. 10.1007/978-3-030-01641-8

4. Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data

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1. Multi-Objective Evolutionary Optimization for Large-Scale Open Pit Mine Scheduling;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

2. Multi-objective Evolutionary Approaches for the Knapsack Problem with Stochastic Profits;Lecture Notes in Computer Science;2024

3. Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting;2023 IEEE Congress on Evolutionary Computation (CEC);2023-07-01

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