In solving many practical mathematicalprogramming applications, it is generally preferable to formulateseveral quantifiably good alternatives that provide very differentapproaches to the particular problem. This is because decisionmakingtypically involves complex problems that are riddled withincompatible performance objectives and possess competingdesign requirements which are very difficult – if not impossible –to quantify and capture at the time that the supporting decisionmodels are constructed. There are invariably unmodelled designissues, not apparent at the time of model construction, which cangreatly impact the acceptability of the model’s solutions.Consequently, it is preferable to generate several alternativesthat provide multiple, disparate perspectives to the problem.These alternatives should possess near-optimal objectivemeasures with respect to all known modelled objective(s), but befundamentally different from each other in terms of the systemstructures characterized by their decision variables. This solutionapproach is referred to as modelling to generate-alternatives(MGA). This paper provides a biologically-inspired simulationoptimizationMGA approach that uses the Firefly Algorithm toefficiently create multiple solution alternatives to stochasticproblems that satisfy required system performance criteria andyet remain maximally different in their decision spaces. Theefficacy of this stochastic MGA method is demonstrated using awaste facility expansion case study
