A genetic algorithm model for optimising long-term open-pit mine production scheduling APCOM 2021

Open-pit mine planning includes the determination of a long-term open-pit mine production scheduling (OMPS) plan. The OMPS plan is generally a three-dimensional (3D) block extraction sequence that seeks to maximise the net present value (NPV) of future cash flows, while complying with technical and operational constraints. The OMPS problem is computationally complex because it involves the analysis of large datasets, while simultaneously incorporating both multiple hard and soft constraints. The OMPS problem has been solved using deterministic or exact approaches, heuristic, and metaheuristic models. When compared to heuristic and metaheuristic models, deterministic approaches tend to take much longer to solve the OMPS problem when the number of constraints increases. Therefore, this paper presents a metaheuristic model which utilises a genetic algorithm (GA) framework to solve the OMPS problem. The GA-based model was applied to the Newman1 and Zuck Small mining instances, which are publicly available from the online MineLib database. The NPVs for the OMPS plan generated using the GA-based model for the two case studies were compared to the best-known published MineLib solutions obtained using the TopoSort heuristic model at the time of this study. For the Newman1 instance the equivalent annual NPVs were approximately US$6.6 million for the GA-based solution and US$5.1 million for the TopoSort solution. For the Zuck Small mining instance, the equivalent annual NPVs were approximately US$99.8 million for the GA-based solution and US$102.5 mil for the TopoSort solution. These results show that the GA-based model produced comparable solutions to some of the best-known solutions, thus, indicating strong potential application of GA-based models to solve OMPS problems.