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Since the open-pit precedence-constrained production scheduling problem is an NP-hard problem, solving it is always a challenging task, especially from a long-term perspective because a mineral deposit containing millions of blocks would require several million precedence arcs as constraints, making the solution time grow exponentially and making a direct approach unfeasible. Therefore, different strategies have been employed since the 1960s to reduce the size of this problem, such as determining the ultimate pit limit, subdividing it into phases, segmenting the production scheduling problem into long-, mid-, and short-term plans, as well as aggregating blocks into clusters, thus significantly reducing the number of precedence arcs. Different modeling and clustering strategies have already been employed in an attempt to reduce the size of the mine sequencing problem, such as layer modeling, re-blocking, bench-phase clustering, or polygon (mining cut) clustering based on a similarity function. The mining cut clustering problem has been solved lately by machine learning and heuristics techniques, and this approach can also introduce operational constraints to the mine sequencing problem, such as equipment size, minimum pit width, and preferential mining direction. In this study, we propose a mining cut clustering model based on Mixed Integer Linear Programming (MILP). Then we solve it by an exact approach and by Constraint Programming (CP), analyzing the strengths and weaknesses of the Constraint Optimization Problem (COP) and Constraint Satisfaction Problem (CSP) techniques. Numerical experiments were carried out on the Bench 15 of the Newman1 dataset, demonstrating the superiority of the COP approach. [ABSTRACT FROM AUTHOR]

An economic feasibility study must consider the uncertainties inherent to a mining project, whose risks must be quantified properly to enable accurate decisionmaking. Studies previously carried out through the Discounted Cash Flow (DCF) methodology in the project evaluated here - a quarry whose operations are currently interrupted, located in Pernambuco, Brazil, formerly taxed under the presumed profit regime - indicated a positive Net Present Value (NPV) in the deterministic scenario, therefore projecting a profitable project. However, a probabilistic analysis using Monte Carlo simulations indicated only a 49.98% occurrence probability for this NPV. An assessment focused on the company's taxation was never carried out, which is a gap that the present study intends to fill, in addition to evaluating the feasibility of immediate investment in this project. Furthermore, this is a gap in Brazilian literature in general, which does not take into account the taxation system in their economic assessments. In this context, considering scenarios whose taxation was based on real and presumed profit regimes, we reassessed the cash flows of this quarry and performed deterministic and probabilistic economic analyses, and compared the results of both scenarios. The sensitivity analysis indicated that the production rate would be the most impactful variable in the project's NPV, considering the six variables assessed. Hence, it was verified in both deterministic and probabilistic analyses that taxation under real profit, results in a higher economic return with a 56.08% probability of the NPV being positive and with the Internal Rate of Return (IRR) higher than inflation (SELIC rate) at 4.81%; the taxation under the presumed profit, on the other hand, obtained respective probabilities of 46.54% and 3.23%. However, with the chances of obtaining some profit (NPV greater than zero) at the order of 50% and a minimal chance of the IRR being greater than the SELIC rate adopted at the time of this study, we would advise against investing in this venture. Moreover, even if the current moment is not the most suitable for investment in this sector, regardless of the production rate assessed in the probabilistic analysis, taxation on the real profit regime presented a greater economic return than taxation on the presumed profit regime., indicating that, for the parameters considered in this study, the first would be the most appropriate choice of tax system for this type of enterprise in Brazil. [ABSTRACT FROM AUTHOR]