Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
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A pulping process is studied to illustrate a new methodology in the field of decision engineering, which relies on the Dominance Rough-Set-based Approach (DRSA) to determine the optimal operating region. The DRSA performs a rough approximation of preferences on a small set of Pareto-optimal experimental points to infer the decision rules with and without considering thresholds of indifference with respect each attribute in the decision table. With thresholds of indifference, each rule can be represented by three discrete values (i.e. 0; 0.5; 1). A value of (1) indicates the first point, in a pair wise comparison, is strictly preferred to the second point from the Pareto domain. A value of (0) indicates the opposite relation whereas a value of (0.5) indicates that the two points are equivalent from an engineering point of view. These decision rules are then applied to the entire set of points representing the Pareto domain. The results show that the rules obtained with the indifference thresholds improve the quality of approximation.