July 22 - July 26: PhD level introduction course in stochastic programming. Over the last few decades these methods have become essential tools for science, engineering, business, computer science, and statistics. An Introductory Tutorial on Stochastic Linear Programming Models Suvrajeet Sen Department of Systems and Industrial Engineering The University of Arizona Tucson, Arizona 85721 Julia L. Higle Department of Systems and Industrial Engineering The University of Arizona Linear programming is a fundamental planning tool. In such cases, it is necessary to address the impact of uncertainty during the planning process. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. Stochastic Optimization Lauren A. Hannah April 4, 2014 1 Introduction Stochastic optimization refers to a collection of methods for minimizing or maximizing an objective function when randomness is present. Stochastic Programming: Tutorial Part I W. Römisch Humboldt-University Berlin Institute of Mathematics 10099 Berlin, Germany ... Two-stage stochastic programming models with recourse Consider a linear program with stochastic parameters of the form min{hc,xi : x ∈ X, T(ξ)x = h(ξ)}, What is Stochastic Programming? The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. Linear programming is a fundamental planning tool. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. An Introduction to Two-Stage Stochastic Mixed-Integer Programming 4 Tutorials in Operations Research, c 2017 INFORMS When the parameters u;v= 1, the model is referred to as the stochastic server location problem (SSLP) [2]; otherwise, it is known as the stochastic server … Fan et al. Lectures on stochastic programming : modeling and theory / Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczynski. A PhD level introduction course to Stochastic Programming is planned. We discuss a variety of LP-based models that can be used for planning under uncertainty. p. cm. Stochastic Integer Programming Jim Luedtke Dept. Probleminstance • problem instance has n = 10, m = 5, d log-normal • certainty-equivalent problem yields upper bound 170.7 • we use Monte Carlo sampling with N = 2000 training samples • validated with M = 10000 validation samples F 0 training 155.7 EE364A — Stochastic Programming 16. -- (MPS-SIAM series on optimization ; 9) The intended audience of the tutorial is optimization practitioners and researchers who wish to acquaint themselves with the fundamental issues that arise when modeling optimization problems as stochastic programs. A two-day introductory series of Tutorials precedes the main conference to provide introduction to some of the central research areas in Stochastic Programming. • Mathematical Programming, alternatively Optimization, is about decision making • Stochastic Programming is about decision making under uncertainty • Can be seen as Mathematical Programming with random parameters It is often (2013) used binary integer linear programming to maximize nurse preferences and hospital regulations. This tutorial is aimed at introducing some basic ideas of stochastic programming. July 27 and 28: Pre-conference tutorials. 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