Combining Simulation-Decomposition, Simulation-Optimization, and Modelling-to-Generate-Alternatives for Planning Under Uncertainty

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Recently, a new exploratory visualization approach, simulation decomposition (SimDec), has been introduced that extends Monte Carlo analysis by enhancing the explanatory power of the cause-effect relationships between multi-variable combinations of inputs and the simulated outputs. SimDec can reveal previously unidentifiable cause-and-effect connections between multi-variable combinations of inputs on the outputs. A SimDec approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used in any analyses that employ simulation models. The proposed research will extend the SimDec method with respect to exploratory data approaches, input/output identification, partitioning, scenario creation, and enhanced visualization techniques, and investigate different applications in large-scale planning settings. Furthermore, using variance decomposition, prior Monte Carlo approaches have calculated indices to evaluate the sensitivities of individual parameters and their interactions on outputs. So-called Sobol analysis determines first-order and total-effect sensitivity indices for the contributions of each input parameter to model output. Along these lines, the proposed study will examine ways to create SimDec-based first-, higher-order, and interaction sensitivity indices - both analytically and numerically - that can be applied not only to SimDec applications, but are also extendable to any Monte Carlo model. Simulation-optimization (SO) provides an optimization approach that incorporates uncertainties expressed as probability distributions. In SO all unknown objectives, 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. Of interest is to concatenate the visual analytic facets of SimDec with the optimization features of SO into a novel, hybrid SimDec-SO method and to apply it to modelling-to-generate-alternatives. Numerous "real world" applications of this SimDec-SO approach will be considered and extensive testing will ascertain its applicability in numerous disparate planning contexts. In efforts to gauge and demonstrate the efficiency improvements, 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, extended supply chains, health care, food processing, agriculture, and environmental sustainability - as well as examining other areas of interest. These applications all possess considerable public importance. Most of this research will involve extensive mathematical and computational testing, while certain applications will necessitate on-site visits to other appropriate industrial and application environments.