AUTHORS: Julian Scott Yeomans
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ABSTRACT: Stochastic optimization problems are often overwhelmed with inconsistent performance requirements and incompatible performance specifications that can be difficult to detect during problem formulation. Therefore, it can prove beneficial to create a set of dissimilar options that provide divergent perspectives to the problem. These alternatives should be near-optimal with respect to the specified objective(s), but be maximally different from each other in the decision region. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). Simulationoptimization approaches are commonly employed to solve computationally difficult problems containing significant stochastic uncertainties. This paper provides a new, stochastic, multicriteria MGA approach that can generate sets of maximally different alternatives for any simulation-optimization method that employs a population-based algorithm.
KEYWORDS: Multicriteria Objectives, Population-based algorithms, Modelling-to-generate-alternativesREFERENCES:
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