The University of Zimbabwe's Faculty of Engineering and the Built Environment houses the Department of Mining Chemical and Metallurgical Engineering, which offers the MSc in Mining Engineering. One of the courses offered is MMIN 506 Research Methodology, taught by Dr. S. Mavengere. The coursework discussed multi-criteria decision making (MCDM), a formalized approach to decision making that involves choosing the best option from a set of alternatives based on several criteria or dimensions.

MCDM is a potent tool utilized to analyze complex problems characterized by multiple and conflicting criteria. Mining engineering problems are particularly complex, often marked by significant uncertainty and the need to evaluate multiple criteria simultaneously. MCDM provides a structured approach to decision making, which ensures that all relevant criteria are considered, thereby ensuring decisions are based on sound reasoning.

Several specific methods are commonly used in MCDM, each possessing unique strengths and weaknesses depending on the nature of the problem being solved. Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Multi-Attribute Utility Theory (MAUT), and Fuzzy Logic are among the most commonly utilized MCDM methods in mining engineering.

AHP involves assigning weights to each criterion or sub-criterion based on their relative importance. Fuzzy logic provides a method for modeling complex systems and decision problems where precise information is not available or too costly to obtain. MAUT is a decision-making approach that develops a utility function to evaluate how desirable different alternatives are based on their attributes. TOPSIS ranks alternatives based on their distance from ideal and anti-ideal solutions, making it useful in balancing conflicting objectives.

To counter the uncertainty that often occurs in MCDM, sensitivity analysis is a common technique used to identify a decision-making model's robustness. Sensitivity analysis evaluates the impact of changes in the input criteria, providing insights into the influence of changes in a single criterion on the final decision and aiding in the identification of critical factors involved in the decision.

One way to evaluate the potential outcomes of a decision is through Monte Carlo simulation, a statistical technique which involves simulating multiple iterations or trials. By using probability distributions of input parameters, this method generates a range of possible outcomes and provides a probabilistic estimate of the results. With Monte Carlo simulation, it is possible to identify the most probable decision scenario and the risks associated with each option.

Bayesian networks are probabilistic graphical models that represent complex relationships between different criteria. These networks consider the probabilistic relationships between factors and can model cause-and-effect relationships among different criteria. They allow us to track uncertainty associated with inputs and decision outcomes, and are particularly useful in decision-making contexts.

Multi-objective evolutionary algorithms (MOEAs) optimize multiple objectives simultaneously, enabling the identification of trade-offs between criteria. Through iterations of selection, reproduction and variation, these algorithms evolve a population of decision solutions, providing a comprehensive understanding of the decision space and the possible outcomes.

Sensitivity analysis, fuzzy logic, Monte Carlo simulation, Bayesian networks and multi-objective evolutionary algorithms are all methods used to counter uncertainty in decision-making contexts. The choice of method depends on the complexity and nature of the decision problem, each having its own advantages and limitations.