Water resource systems face uncertainties from climate variability, demand fluctuations and policy shifts. Stochastic optimisation offers mathematical frameworks to plan reservoir operations, ...

Robust stochastic optimisation methods seek decision rules that perform reliably under both inherent randomness and ambiguity in probability models. Combining classical stochastic programming—where ...

A global research team led by scientists from China’s Tianjin Renai College has developed a novel stochastic optimization technique for enhanced dispatching and operational efficiency in PV-powered ...

Course in stochastic optimization with an emphasis on formulating, solving, and approximating optimization models under uncertainty. Topics include: Models and applications: extensions of the linear ...

In this paper we study the problems of pricing and optimizing sidecar and collateralized reinsurance portfolios. The academic literature on sidecar portfolio optimization that takes into account the ...

Research areas: Healthcare optimization under uncertainty, Large-scale optimization, stochastic programming, decomposition-based integer programming algorithms (Benders decomposition, Lagrangian ...