Linear programming is a fundamental planning tool. 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 … A two-day introductory series of Tutorials precedes the main conference to provide introduction to some of the central research areas in Stochastic Programming. We discuss a variety of LP-based models that can be used for planning under uncertainty. -- (MPS-SIAM series on optimization ; 9) 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. 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. of Industrial and Systems Engineering Wisconsin Institute for Discovery University of Wisconsin-Madison, USA jim.luedtke@wisc.edu XIV International Conference on Stochastic Programming Buzios, Brazil June 25, 2016 It is often July 22 - July 26: PhD level introduction course in stochastic programming. Stochastic Integer Programming Jim Luedtke Dept. Fan et al. What is Stochastic Programming? In such cases, it is necessary to address the impact of uncertainty during the planning process. Over the last few decades these methods have become essential tools for science, engineering, business, computer science, and statistics. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. p. cm. This tutorial is aimed at introducing some basic ideas of stochastic programming. 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 Punnakitikashem, Rosenberger, and Buckley Behan (2008) proposed a stochastic integer programming model for NSP to minimize the workload penalty on nurses and satisfy the expected demands in the planning horizon. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. • 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 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)}, July 27 and 28: Pre-conference tutorials. The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. Lectures on stochastic programming : modeling and theory / Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczynski. 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