Multidimensional stochastic mine planning and design for sustainable mining *J. Skiba1, L. Azevedo1, M. Bascompta2, A. de Sousa1 1CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Portugal (*Presenting author: jakub.skiba@tecnico.ulisboa.pt) 2Universitat Politècnica de Catalunya, Spain ABSTRACT Stochastic mine planning methods have gained significant traction in recent years, promising more resilient mining extraction operations that can stand the test of uncertain mineral grades and market fluctuations. Through the incorporation of a probabilistic approach in the planning and mine design, these methods have proven to be more adaptable to variations of commodity prices, changing legislative frameworks, and natural ore body heterogeneity in comparison to traditional, deterministic methods. This makes them particularly well-suited to the future needs of the mining industry, as it encounters challenges of increased market volatility and the shift to the extraction of more complex and deeper ore bodies. Simultaneously, the pressure rises for the mining companies to develop solutions that would facilitate more efficient and environmentally friendly mineral extraction, while limiting the socio-economic impacts on the local communities. While machine learning and probabilistic planning methods appear to be promising tools for the mitigation of these impacts, the implementation of solutions in the industry is still limited. The creation of more resilient mining operations that can adapt to changing market conditions and grade variability, while addressing socio-environmental issues, would result in an industry that is not only more profitable but also socially acceptable. The work presented herein depicts current developments in the field of incorporating environmental, social, and governance (ESG) aspects into mine planning and design, while highlighting existing research gaps and investigating opportunities for the application of stochastic mine planning methods in this area. KEYWORDS Stochastic mine planning, sustainability, waste management, energy efficiency, fleet optimization 1. Introduction Prior to mine planning, an identified ore deposit is characterized by a drilling campaign, which samples the orebody in various established locations to determine its key characteristics and spatial distribution. Those include, among many others, rock type, chemical composition, and grades. Based on the obtained information, a three-dimensional
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