A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
The Resource-Constrained Project Scheduling Problem(RCPSP) is a significant challenge in highly regulated industries, such as pharmaceuticals and agrochemicals, where a large numb...
Ranking is a fundamental operation in data analysis and decision support, and plays an even more crucial role if the dataset being explored exhibits uncertainty. This has led to m...
Polynomial chaos theory (PCT) has been proven to be an efficient and effective way to represent and propagate uncertainty through system models and algorithms in general. In partic...
We analyze volume flexibility — the ability to produce above/below the installed capacity for a product — under endogenous pricing in a two-product setting. We discover that ...