Table 3. Literature review of mining polygons modelling.
| Dig-limit polygons Approaches | Authors | Results | |
|---|---|---|---|
| Clustering Techniques | Hierarchical clustering | ( Tabesh and Askari-Nasab 2011; Tabesh and Askari-Nasab 2013) | Cluster shapes not controlled and non-practical clustering patterns and similarity index takes only one element into account |
| ( Tabesh and Askari-Nasab 2019) | Cluster shapes-controlled similarity index takes into account multi elements and grade uncertainty | ||
| K-Means Clustering | ( Salman et al. 2021) | Mineable cluster shapes, high destination homogeneity, rock unity. Possibility to extend complex multi-element, multi-destination deposits and to incorporate grade uncertainty | |
| Dig-limit optimizations | Genetic algorithms | ( Ruiseco et al. 2016) | The dig-limits constraints equipment incorporated, performs better than simulated annealing and works with multiple rock types, processes and metals |
| Simulated annealing | ( Norrena and Deutsch 2001; P et al. 2002; Neufeld et al. 2003) | Dig-limits constraints incorporated, maximizes the profit and penalizes smaller angles of operation taking into account the digability. Solution space search algorithm moves toward non-improving solutions with a certain probability. | |
| Grade control strategy of SMUs using Mixed Integer Linear Programming | ( Sari and Kumral 2018; Nelis et al. 2022; Nelis and Morales 2022) | Dig-limits constraints incorporated by Sari and Kumral (2018), cut-off grades not used and the model minimizes the loss associated with misclassification of SMU’s | |
| Greedy search: Feasibility grade control
Floating Circle algorithm |
( Wilde and Deutsch 2007) | Requires an initial solution and attempts to optimize the profit iteratively by re-arranging the form of blocks accumulated into units. | |
| Local search algorithm | ( Richmond and Beasley 2004) | Dig-limits constraints incorporated, minimize ore loss and mining dilution by using a payoff function per block | |
| Heuristic approach: Floating cone algorithm | ( Richmond and Beasley 2004; Vasylchuk and Deutsch 2019) | Dig-limits constraints incorporated, minimize ore loss and dilution. Multiple ore types and objective functions taken into account to maximize the net present value (NPV), no guarantee of optimality. Dig-limit optimization in 2-D |