Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Optimizing The Environmental And Economic Sustainability Of Contingency Base Infrastructure, Jamie E. Filer Mar 2020

Optimizing The Environmental And Economic Sustainability Of Contingency Base Infrastructure, Jamie E. Filer

Theses and Dissertations

Contingency bases are often located in remote areas with limited access to established infrastructure grids. This isolation leads to standalone systems comprised of inefficient, resource-dependent infrastructure, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. Planners can mitigate these negative impacts by selecting sustainable technologies. However, such alternatives often come at a higher procurement cost and mobilization requirement. Accordingly, this study aims to develop and implement a novel infrastructure sustainability assessment model capable of optimizing the tradeoffs between environmental and economic performance of infrastructure alternatives.


Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun Mar 2020

Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun

Theses and Dissertations

Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in …