Managing uncertainty is a main challenge within supply chain management. Therefore, it is
expected that those supply chain planning methods which do not include uncertainty obtain …

This tutorial is aimed at introducing some basic ideas of stochastic programming. The
intended audience of the tutorial is optimization practitioners and researchers who wish to …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

With new global regulations on supply chains (SCs), sustainable regulation mechanisms
have become subject to controversy. The intention is to create and expand green and …

This paper presents a robust network design model for the supply of blood during and after
disasters. A practical optimization model is developed that can assist in blood facility …

The aim of stochastic programming is to find optimal decisions in problems which involve
uncertain data. This field is currently develo** rapidly with contributions from many …

Conic optimization is a significant and thriving research area within the optimization
community. Conic optimization is the general class of problems concerned with optimizing a …

A complete and accessible introduction to the real-world applications of approximate
dynamic programming With the growing levels of sophistication in modern-day operations, it …

For ma. ny problems in reliability and optimization it is necessary to calculate the
probabilities of! arge deviations of normal random vectors. Using the structure of the normal …

Today, due to the increasing environmental hazards and governmental regulations, as well
as the limitation of sources of production, researchers have paid special attention to the …