**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-alternatives

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