Decision Support Performance Research Articles

Development of risk-informed, performance-based asset management in mining

This paper discusses possibilities of developing a holistic risk-informed, performance-based asset management in mining (RIPBAMM). This process would consist of modelling and probabilistic quantification regarding decision support performance indicators. It assists decision-makers in determining not only which mine improvement investment options should be implemented, but also how to prioritize resources for their implementation based on their predicted levels of profitability. The RIPBAMM approach will complement and integrate existing main mine activities such as exploration, ore body modelling, mine design, planning and scheduling, exploitation (all the phases of the mine life), mineral treatment, cost and market model, operational safety and health, environmental issues, mining equipment reliability and maintenance process, equipment selection model, security, etc. RIPBAMM will involve an integrated assessment of dominant influence factors and performance measures related to mining operations. This process is intended to maximize both net present value (NPV) of the mine, and long-term profitability through a continuous support to a decision-making process. It may be particularly useful while optimizing several mine sites belonging to the same mining company. Initial risk informed asset management (RIAM) applications have been initially developed for the nuclear power industry. Afterwards, this process has been adapted to provide decision-making support to other types of power stations, complex facilities (usually capital-intensive), or even groups of such facilities across a wide variety of industries. RIPBAMM is introducing numerous (stochastic) models and supporting performance metrics that can ultimately be employed in order to support decisions that affect the allocation and management of mine resources (i.e. financial support, employment, scheduling, etc.).

Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis

This paper describes the inductive learning methods for generating decision rules in decision support systems. Three similarity-based learning systems are studied based on: (1) the AQ-Star method, (2) the Tree-Induction method, and (3) the Probabilistic Learning method. Loan evaluation examples and empirical data are used as a basis for comparing these inductive learning methods on their algorithmic characteristics and decision support performance.