We present some models to find the best allocation of a limited amount of so-called runningtime supplements (extra minutes added to a timetable to reduce delays) on a railway line. Bythe best allocation, we mean the solution ...

Multistage stochastic programs, which involve sequences of decisions over time, areusually hard to solve in realistically sized problems. In the two-stage case, several approaches basedon different levels of available ...

Convexity arises quite naturally in financial risk management. In riskpreferences concerning random cash-flows, convexity corresponds to thefundamental diversification principle. Convexity is a basic property alsoof budget ...

Multi-stage stochastic programs (MSP) pose some of the more challenging optimizationproblems. Because such models can become rather intractable in general, it is important todesign algorithms that can provide approximations ...

We develop a new modeling and exact solution method for stochastic programming problems thatinclude a joint probabilistic constraint in which the multi-row random technology matrix is discretely distributed. We binarize ...

Quasi-Monte Carlo algorithms are studied for designing discrete approximationsof two-stage linear stochastic programs. Their integrands are piecewiselinear, but neither smooth nor lie in the function spaces considered for ...

We consider the solution of a system of stochastic generalized equations (SGE) where theunderlying functions are mathematical expectation of random set-valued mappings. SGE hasmany applications such as characterizing ...

We consider risk-averse formulations of multistage stochastic linear programs. Forthese formulations, based on convex combinations of spectral risk measures, risk-averse dynamicprogramming equations can be written. As a ...

We provide lower estimates for the norm of gradients of Gaussian distribution functions and apply the results obtained to a special class ofprobabilistically constrained optimization problems. In particular, it is shown ...