ABSTRACT
Management decisions often involve choosing the “best” or the “most-preferred” option among a set of competing alternatives. In decision theory, entropy is used to derive objective measures of the relative importance of each criterion as it influences the performance of competing alternatives. In general, selection algorithms produce rankings of options from a finite set of competing alternatives as a function of how each alternative performs across multiple evaluation criteria. For ranking risk criticality, a cardinal-based approach is frequently employed. This approach provides the flexibility to join optimization protocols with ranking algorithms when optimal assignments of risk reduction resources, under a variety of constraints, need to be determined. The quantified effect on a C-Node’s operability if, due to the realization of risks, one or more contributing programs or supplier–provider chains degrade, fail, or are eliminated. A C-Node’s risk-reduction benefit expected from expending its risk mitigation dollars.
