Obtaining robust decisions under uncertainty by sensitivity analysis on OWA operator

The successful design and application of the ordered weighted averaging (OWA) method as a decision making tool depends on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability methods which give different behavior patterns for OWA. These methods will be compared by using sensitivity analysis on the outputs of OWA with respect to the optimism degree of the decision maker. The theoretical results are illustrated in a water resources management problem. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree and therefore it has better computation efficiency. A simulation study is also reported in this paper, where the dependence of the optimal decision on the uncertainty level is examined. Also based on obtained sensitivity measure, a new combined measure of goodness has been defined to have more reliability in obtaining optimal solutions