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Recent developments in modelling and optimization approaches for the production of mineral and energy resources have resulted in new simultaneous stochastic optimization frameworks and related digital technologies. A mining complex is a type of value chain whereby raw materials (minerals) extracted from various mineral deposits are transformed into a set of sellable products, using the available processing streams. The supply of materials extracted from a group of mines represents a major source of uncertainty in mining operations and mineral value chains. The simultaneous stochastic optimization of mining complexes, presented herein, aims to address major limitations of past approaches by modelling and optimizing several interrelated aspects of the mineral value chain in a single model. This single optimization model integrates material extraction from a set of sources along with their uncertainty, the related risk management, blending, stockpiling, non-linear transformations that occur in the available processing streams, the utilization of processing streams, and, finally, the transportation of products to customers. Uncertainty in materials extracted from the related mineral deposits of a mining complex is represented by a group of stochastic simulations. This paper presents a two-stage stochastic mixed integer nonlinear programming formulation for modelling and optimizing a mining complex, along with a metaheuristic-based solver that facilitates the practical optimization of exceptionally large mathematical formulations. The distinct advantages of the approach presented herein are demonstrated through two case studies, where the stochastic framework is compared to past approaches that ignore uncertainty. Results demonstrate major improvements in both meeting forecasted production targets and net present value. Concepts and methods presented in this paper for the simultaneous stochastic opti-B Roussos Dimitrakopoulos

Conventional open pit mine design methods ignore geological uncertainty in terms of metal content and material types which can impact the quantities processed in multiple process mining operations. A stochastic framework permits the use of geological simulations to quantify geological uncertainty; however, existing models have either not been extended to pushback design for mines with multiple elements, multiple materials and multiple destinations, or are limited in their ability to incorporate joint local uncertainty represented through sets of geological simulations. This work aims to integrate grade and material uncertainty in pushback design for open pit mines. Two formulations are proposed to modify existing pushback designs to reduce risk in terms of the amounts of material going to each destination, while maintaining similar pushback sizes when compared to the original design. The proposed formulations are applied at BHP Billiton's Escondida Norte mine, Chile, and show a 35-61% reduction in variability in terms of quantities of material sent to the various processes.

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In the cybersecurity research community, there is no one-size-fits-all solution for merging large numbers of heterogeneous resources and experimentation capabilities from disparate specialized testbeds into integrated experiments. The current landscape for cyber-experimentation is diverse, encompassing many fields including critical infrastructure, enterprise IT, cyber-physical systems, cellular networks, automotive platforms, IoT and industrial control systems. Existing federated testbeds are constricted in design to predefined domains of applicability, lacking the systematic ability to integrate the burgeoning number of heterogeneous devices or tools that enable their effective use for experimentation. We have developed the Merge architecture to dynamically integrate disparate testbeds in a logically centralized way that allows researchers to effectively discover, and use the resources and capabilities provided the by evolving ecosystem of distributed testbeds for the development of rigorous and high-fidelity cybersecurity experiments.