Optimization-based Decision Support for Integrated Mining Operations

Large-scale mining operations, which use the truck and shovel technology, are faced with a complex set of interdependent decision: 1) deployment of resources (i.e., trucks and shovels) to various locations; 2) scheduling of maintenance activities to ensure the long-term, safe and efficient operation of the equipment. These decisions are made in response to changing of mine conditions and downstream processing demands and are further complicated by the need to take uncertain information (e.g., equipment and road conditions, the near-term weather) into account to achieve a robust system that can handle small changes, yet is responsive to significant upsets. Individual decisions may be made by different personnel, who are often members of different business units (e.g., production, maintenance, etc.), with minimal consultation with the other stakeholders and despite the potential scope of impact of the decisions. Many, if not most, integrated mining operations have no framework for coordinating the decision-making process and for arbitrating between conflicting decisions; however, the consensus opinion is that coordinated decisions must be made ensure the maximum long-term economic benefit of the company.

Our work is focused on development of an optimization-based decision support system, which draws on techniques from vector (multi-objective or multi-criteria) optimization theory, post-optimality analysis, visualization, modelling and simulation. The work involves an interdisciplinary team of engineers, computing scientists and mathematicians.