Simulation-Optimization Methods and Modelling-to-Generate Alternatives for Planning Under Uncertainty

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Comprehensive planning problems prove particularly complicated when their numerous system components exhibit considerable degrees of uncertainty. This complexity is frequently compounded by numerous stochastic elements that cannot be definitively ascertained beforehand. Often precise analytical formulations do not exist and, even when they do, contain a multitude of highly stochastic, non-linear components. Determining good solutions to such large stochastic problems, while simultaneously incorporating the inherent uncertainties, can prove exceedingly difficult. Irrespective of such difficulties, this planning must be performed in virtually every sphere of "real world" decision-making, planning, and policy-formulation. Simulation-optimization (SO) provides an optimization approach that incorporates uncertainties expressed as probability distributions for constructing best solutions to complex planning problems. In SO all unknown objective(s), constraints, and parameters are replaced by simulation models in which the decision variables provide the settings under which each simulation experiment is run. An efficient optimization component guides the solution exploration through the feasible domain performing only a limited number of simulations. While some of my previous research has demonstrated that SO can produce impressive results in large-scale planning applications, the solution time/quality of the procedure can vary considerably from one implementation to another - thereby limiting its universal applicability for all planning purposes. Improved search procedures will be sought. My research will investigate a variety of approaches for improving the performance of SO by decreasing the solution time and/or producing better quality solutions for the aforementioned large-scale decision making and planning problems. ***Numerous "real world" applications of the procedure will be considered and extensive testing will be employed to ascertain SO's suitability (and, just as importantly, its non-suitability) in disparate planning environments. In efforts to gauge and demonstrate the efficiency improvements from the new approaches, I will continue with data from several earlier case studies that have used this technique in such diverse settings as municipal solid waste planning, reverse logistics & remanufacturing of leaded waste products, planning & management of the extended supply chain, and environmental sustainability. These applications all possess considerable public and environmental importance. Several novel applications for SO will also be sought and the benefits of its application will be demonstrated. Additional research will extend the method (and all its newly-found efficiencies) into multi-objective problem situations that contain considerable uncertainty. This will provide significant theoretical and practical advances.**