A mining complex or mineral value chain can be seen as a system where raw materials are extracted from one or several mineral deposits, transformed into sellable products using different processing streams, and delivered to the spot-market or customers. The supply of materials extracted from the mines represents a major source of uncertainty, referred to as geological uncertainty, and entails technical risk that must be managed. In the last decade, there has been a sustained development of models that integrate different components and aspects of mining complexes to simultaneously optimize mining, processing, stockpiling and transportation decisions while explicitly accounting for geological uncertainty, as this is essential to improving the overall mineral chain performance. In doing so, researchers have employed advanced optimization techniques to address the computational complexity of the resulting large-scale optimization problems and better incorporate endogenous and exogenous uncertainties in the key parameters (geological/mining, financial). Examples from main applications demonstrate improvements in technical risk management, blending, stockpiling, and capital investments, as well as generation of more robust schedules through better-informed decision-making. This talk surveys recent research in simultaneous stochastic optimization of mining complexes and presents a review of applications, solution methods, and key findings.
In the last 20 years, there has been increasing interest in using advanced optimization techniques to develop and manage mineral resources, to make informed decisions regarding potential risks, and to maximize value and minimize costs while satisfying the various requirements and limitations of the particular operation. More specifically, models that account for geological uncertainty and integrate the various interacting aspects of an operation that were treated separately in the past have been devised. To address such large and complex problems, researchers have employed metaheuristics, or methods combining (meta)heuristics with exact methods or machine learning techniques. The use of these approaches substantially increases the size of the instances that can be solved. This talk presents several of such solution methods.
School of Management, University of Quebec, Canada
Affiliated with the COSMO Stochastic Mine Planning Laboratory, McGill University, Canada
After receiving a BSc and a Master’s in Applied Mathematics, Amina Lamghari obtained a PhD in Operations Research from the University of Montreal, Canada, after which she worked as a post-doctoral fellow and later as a research associate at the COSMO Stochastic Mine Planning Laboratory at McGill University. Amina is currently an assistant professor in the Management School at the University of Quebec. Her research interests are centered on various techniques and algorithms —(meta)heuristics, hyper-heuristics, and matheuristics — for optimization and their integration and application to solve complex scheduling and planning problems in an efficient manner. All of her work to date has had direct real-world applications. During the last nine years, applications in mine planning have been the main focus of her research, undertaken in response to the needs of the mining industry for more efficient and robust production schedules and operations plans. Many of the algorithms she developed were incorporated in the COSMO Suite software, a platform designed to aid in the dissemination of new stochastic mine planning optimization algorithms, techniques, and models to the mining industry.