The utility of land-cover change data is often derived from the intersection with other information, such as riparian buffers zones or other areas of conservation concern. In order to avoid error propagation, we wanted to optimize our change maps to have very low error rates. Our accuracy optimization methods doubled the number of total change locations mapped, and also increased the area of de...

this paper discusses the portfolio selection based on robust optimization. since the parameters values of the portfolio optimization problem such as price of the stock, dividends, returns, etc. of per share are unknown, variable and their distributions are uncertain because of the market and price volatility, therefore, there is a need for the development and application of methodologies for de...

in this study, an optimization model is proposed to design a global supply chain (gsc) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of economic cooperation organization trade agreement (ecota) is...

Reliability analysis and trade-offs between safety cost with insufficient data represent an inevitable problem during the early stage of structural design. In this paper, efficient uncertainty theory-based reliability a design method are proposed under epistemic uncertainty. The factors influencing structure regarded as uncertain variables. Based on this, new metric termed measure is employed t...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...

Two non-intrusive uncertainty propagation approaches are proposed for the performance analysis of engineering systems described by expensive-to-evaluate deterministic computer models with parameters defined as interval variables. These employ a machine learning based optimization strategy, so-called Bayesian optimization, evaluating upper and lower bounds generic response variable over set poss...

This paper presents a comprehensive methodology that combines uncertainty quantification, propagation and robustness-based design optimization using a Bayesian framework. Two types of epistemic uncertainty regarding model inputs/parameters are emphasized: (1) uncertainty modeled as p-box, and (2) uncertainty modeled as interval data. A Bayesian approach is used to calibrate the uncertainty mode...