While methods for optimization under uncertainty have been studied intensely over the past
decades, the explicit consideration of the interplay between uncertainty and time has gained …

Stochastic optimization refers to a collection of methods for minimizing or maximizing an
objective function when randomness is present. Over the last few decades these methods …

This paper aims at resolving a major obstacle to practical usage of time-consistent risk-
averse decision models. The recursive objective function, generally used to ensure time …

Choosing the appropriate sample size in Sample Average Approximation (SAA) method is
very challenging. Inappropriate sample size can lead to the generation of low quality …

We consider the last mile delivery system with roaming delivery locations and stochastic
travel times. The problem is formulated as a two-stage stochastic programming model with …

This chapter describes various methods for solving optimal portfolio and asset-liability
management models as discrete-time stochastic programs. The focus is on solution methods …

Reducing greenhouse gas emissions is now an important global issue. The promotion and
application of low-carbon technologies is one important method of achieving greenhouse …

As the need for increased renewable energy generation to meet climate goals grows, so
does the demand for critical minerals in the industrial sector. This raises concerns about the …

Dynamic portfolio optimization has a vast literature exploring different simplifications by
virtue of computational tractability of the problem. Previous works provide solution methods …

In this paper, we present an Uzawa-based heuristic that is adapted to certain type of
stochastic optimal control problems. More precisely, we consider dynamical systems that …