= → ⋅ ∑( , ⋅ ⋅ ( − )) (1) − ⋅ ( )− ⋅ ( ) =1 ℎ → − ⋅ ( ) where is a block, ∀ ∊ ; is the value of the block ($); is the block mass (t); is an element, ∀ ∊ ; and are the selling price ($/t) and selling cost ($/t) of an element ∊ , respectively; , is the grade (%) of an element within block ; is the recovery (%) of an element ∊ ; is the mining cost of the block ($/t); and is the processing cost of the block ($/t). This approach considers that two blocks could individually be considered ore and waste rock, but disregards the possibility that blending the blocks might cause both to be considered ore. In addition, this approach considers that mining and processing costs would be fixed, as well as the recovery, which is a simplification of reality, as different blocks with the same grade may behave differently when processed due to geometallurgical variables such as recovery and specific energy (Mazzinghy et al., 2025). Geometallurgy is an interdisciplinary approach that focuses on the spatial characterisation of different materials and domains within a deposit in order to consider their impact on mining, processing, and environmental aspects. This approach integrates geological, mineralogical, mining, metallurgical, environmental, and economic parameters, with the aim of reducing technical and operational risks during project evaluation and production. Ultimately, geometallurgy allows for the maximisation of the economic value of a mining project by characterising different material types within the deposit to predict project and operational outcomes through a comprehensive understanding of the data generated (GMG, 2025). In recent years, several studies have been developed incorporating geometallurgical variables into mine planning. Morales et al. (2019) proposed a stochastic long-term mine planning framework that incorporates geometallurgical uncertainty through equiprobable scenarios and stochastic integer programming (SIP) to solve the mine sequencing problem as DBS approach while managing risk. Results showed that explicitly accounting for geometallurgical variability affects pit limits, production schedules, and ultimately the financial performance of the mining project. Da Mata et al. (2023) proposed the integration of block-level geometallurgical variables (recovery and specific energy) into the mine sequencing framework, demonstrating their significant impact on NPV and the reliability of mine planning decisions. Martins et al. (2025) compared two spatial interpolation strategies to generate a geometallurgical block model: i) comminution indices as a nonadditive variable, and ii) specific energy as an additive variable. Then, the DBS approach was used in both geometallurgical block models and the results obtained for this case study show that both yield similar mine life and NPV, with negligible differences in planning outcomes. Paredes et al. (2025) proposed a DBS-based framework that explicitly compares constant, categorical, and block-level variable metallurgical recovery models in open-pit strategic mine planning. The results show that incorporating block-level recovery variability improves NPV, reduces economic bias, and leads to more robust and reliable planning decisions. This study investigates the role of geometallurgical variables in the development of more robust mine planning practices, aiming to address production flow with greater precision and reduce risks. In this sense, four scenarios are generated: i) the first is a
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