An Efficient Pruning Method for Decision Alternatives of OWA Operators

In this paper, we present an efficient method for pruning decision alternatives in the case of using ordered weighted averaging (OWA) operators for decision making. The proposed method helps to identify inferior alternatives that are less likely to be selected out of competing alternatives as the OWA aggregation proceeds. It thus enables us to diminish the number of alternatives before applying the OWA operators. The reordering process unique to the OWA aggregation plays an important role in identifying inferior alternatives. The efficacy of the proposed method is demonstrated by simulation analysis in which artificial decision problems of diverse sizes are generated and then examined with four scenarios: pruning alternatives, pruning alternatives with rank-order OWA weights, pruning alternatives with a normalized decision problem, and pruning alternatives with both a normalized decision problem and rank-order OWA weights. The proposed method is easy to use, and the simulation results show that the number of alternatives can be reduced drastically by applying this method.