103 Table 7 – Average client contract fulfillment over 14-day simulations for each solver. Approach P10 (%) P50 (%) P90 (%) Greedy 78.0 89.8 96.4 MCTS 89.5 95.7 99.3 ggMCTS 88.5 96.1 99.1 4.3. Solver Decision-Making The blending network along with the flow assignments chosen by the MCTS solver averaged over the representative simulation (the 14-day simulation in which MCTS had median performance relative to the greedy benchmark) are plotted in Figure 5. The solution exhibits a simple yet effective blending pattern, with the thickness of edges proportional to the amount of material sent in that flow. Each node reports the average composition leaving that node. (The process node labels use shorthand to describe the processing pathways: “Wash” refers to washing only, “Float” refers to the addition of flotation, and “Grind” refers to the addition of grinding.) Figure 5. One MCTS flow assignment during the representative simulation. In comparison, the same plot for the greedy benchmark (not shown here) sends all mine outputs to one pile, blending them together, and then sends that material almost exclusively to flotation. Given that the greedy algorithm can solve deterministic problems to optimality, this result implies that the optimal one-day solution in this scenario is to blend all feedstocks together (since there is an even and equal distribution of low and high quality feedstocks) and primarily float them, which is largely consistent with industry approaches. 5. DISCUSSION The two scenarios demonstrate the importance of planning ahead given the uncertainty that arises from feedstock and process variability. When the feedstock changes day-by-day, as in Scenario A, the
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