Energy system optimization models (ESOMs) have been used extensively in providing
insights to decision makers on issues related to climate and energy policy. However, there is …

In this introductory chapter we discuss some basic approaches to modeling of stochastic
optimization problems. We start with motivating examples and then proceed to formulation of …

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

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Online prediction methods are typically presented as serial algorithms running on a single
processor. However, in the age of web-scale prediction problems, it is increasingly common …

This book is open access under a CC BY-NC 4.0 license. This revised, updated textbook
presents a systems approach to the planning, management, and operation of water …

Fuzzy set theory was first developed for solving the imprecise/vague problems in the field of
artificial intelligence, especially for imprecise reasoning and modelling linguistic terms. In …

This chapter reviews the principles of sample average approximation (SAA) for solving
simulation optimization problems. We provide an accessible overview of the area and …

In this paper, we provide a general classification of mathematical optimization problems,
followed by a matrix of applications that shows the areas in which these problems have …

In this paper reliability-based optimization problems in structural engineering are formulated
on the basis of the classical decision theory. Several formulations are presented: Reliability …