the grades are highlighted based on material classification: red for waste, and yellow for ore. P50 column represents mean grades from the grade distribution modelling, for comparison purposes. Table 5 – Muck piles’ mean grade values (Cu%) for each scenario under analysis. Muck pile ID P5 Scenario 1 P10 Scenario 2 P30 Scenario 3 P50 Scenario 4 P70 Scenario 5 P90 Scenario 6 P95 Scenario 7 1 - - - - - - - 2 0.3382% 0.3386% 0.3394% 0.3400% 0.3405% 0.3414% 0.3418% 3 0.3207% 0.3228% 0.3271% 0.3300% 0.3330% 0.3373% 0.3394% 4 0.2567% 0.2617% 0.2724% 0.2798% 0.2874% 0.2986% 0.3041% 5 0.2789% 0.2806% 0.2847% 0.2881% 0.2923% 0.3008% 0.3069% 6 0.2361% 0.2391% 0.2456% 0.2499% 0.2544% 0.2609% 0.2641% 7 0.3599% 0.3642% 0.3733% 0.3797% 0.3864% 0.3964% 0.4015% 8 0.2408% 0.2449% 0.2538% 0.2601% 0.2667% 0.2766% 0.2815% 9 0.3287% 0.3333% 0.3428% 0.3498% 0.3567% 0.3672% 0.3725% 3.3.Results For the implementation, four consecutive periods were evaluated to represent the regular activities over two days (each period represents a 10-hr shift). The solver selected for the task was Gurobi 12.0.0 executed in AMPL software. As mentioned before, when categories are fixed based on a break-even cutoff grade in either ore or waste, marginal material is labeled as waste and the corresponding grade is replaced by zero metal content. When this happens, the model is forced to only consider ore-labeled muck piles for possible allocations to satisfy processing facility requirements, even if tonnages are insufficient or grades are away from the target objectives. Table 6 reports the summary results for this implementation over the seven scenarios proposed. From P5 to P70, the results are similar in performance and are only differentiable by the increments generated by the metal content increase and small variances in the truck allocations (P50 highlighted in yellow represents the average case). This is because there is no significant change in the muck piles’ categorization for the shovels to be dynamically allocated, where there is always a pre-requisite to strip at least one of the waste-categorized muck piles to have access to enough ore to feed the mill. In contrast, P90 and P95 (best case scenarios) break the uniformity by proposing a plan where waste stripping is not needed immediately to satisfy processing facility requirements. Therefore, truck fleet use is reduced, penalties in ore production are dismissed, and total revenue is substantially increased. Table 6 – Summary results for fixed categories approach. KPIs P5 Scenario1 P10 Scenario2 P30 Scenario3 P50 Scenario4 P70 Scenario5 P90 Scenario6 P95 Scenario7 Ore Production (tons) 80,000 80,000 80,000 80,000 80,000 100,000 100,000 Waste Stripping (tons) 30,000 30,000 30,000 30,000 30,000 0 0 Average Grade (%Cu) 0.3491% 0.3514% 0.3564% 0.3600% 0.3635% 0.3289% 0.3313%
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