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AbstractIn this paper we consider decision problems that can be described as linear decision models. These models have been traditionally solved using linear programming, fuzzy linear programming, multiple‐objective linear programming or 'what‐if' analysis. Using these approaches, one encounters a number of difficulties. We propose an 'evolutionary approach' to overcome these difficulties. In the proposed approach the decision maker does not have to precisely specify the model (i.e. the objective functions, the RHS values, etc.) at the beginning of the solution procedure. In fact, the model evolves as the solution procedure proceeds.

AbstractThe problem of finding a strict total order for a finite set of multiple criteria alternatives is considered. Our research extends previous work by us, which considered finding a partial order for a finite set of alternatives. We merge the preference information extracted from the preference cones and corresponding polyhedral sets, with the information derived from pairwise comparisons of two alternatives, yielding a preference matrix. This preference matrix is used as input to an integer programming model to obtain a strict total order that provides a transitive ranking for the set of alternatives. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 155–163, 2014