Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlap** notational systems with …

This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

The Springer Series in Operations Research and Financial Engineering publishes
monographs and textbooks on important topics in theory and practice of Operations …

We propose a novel, optimization-based method that takes into account the objective and
problem structure for reducing the number of scenarios, m, needed for solving two-stage …

In this paper, we develop a risk-averse two-stage stochastic program (RTSP) that explicitly
incorporates the distributional ambiguity covering both discrete and continuous distributions …

We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel
times and one with stochastic delivery locations. Both variants are formulated as a two-stage …

Electric vehicles (EVs) hold promise to improve the energy efficiency and environmental
impacts of transportation. However, widespread EV use can impose significant stress on …

We consider unconstrained optimization problems where only “stochastic” estimates of the
objective function are observable as replicates from a Monte Carlo oracle. The Monte Carlo …

We consider a risk-averse multi-stage stochastic program using conditional value at risk as
the risk measure. The underlying random process is assumed to be stage-wise …

Optimisation under uncertainty has always been a focal point within the Process Systems
Engineering (PSE) research agenda. In particular, the efficient manipulation of large amount …