Fuzzy Decision Making in Business Intelligence
The purpose of this study was to investigate and implement fuzzy decision algorithms based on unequal objectives and minimization of regret to retrieve an optimal decision in business intelligence. Another aim was to compare these two models; those have been applied in business intelligence area. The use of unequal objectives and minimization of regret methods based on the essential concept of the fuzzy decision were applied on the business intelligence model. The first method uses Saaty’s approach of comparison objectives to determine the weight of the objectives, while the second method uses the regression of objectives which acts as a filter for high values by divesting them of their decisive power. In a complex business problem, we have used knowledge of experts in verbal expressions, converted these verbal expressions into linguistic variables and then used fuzzy decision making models to retrieve best decision. The implementation’s results of the two methods were the same regarding to the final decision set. The first model results indicated the effect of the influential factors on the products, while the second model results showed the payoff for the influential factors and its effectives on the products.
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