Now showing items 231-234 of 234
Multistage Stochastic Decomposition: A Bridge between Stochastic Programming and Approximate Dynamic Programming
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 ...
Optimizing existing railway timetables by means of stochastic programming
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 ...
Gradient estimates for Gaussian distribution functions: Application to probabilistically constrained optimization problems
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 ...
SDDP for multistage stochastic linear programs based on spectral risk measures
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 ...