We discuss a variety of LP-based models that can be used for planning under uncertainty. • 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 What is Stochastic Programming? It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. (2013) used binary integer linear programming to maximize nurse preferences and hospital regulations. -- (MPS-SIAM series on optimization ; 9) This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. July 22 - July 26: PhD level introduction course in stochastic programming. 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 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. It is often Stochastic Integer Programming Jim Luedtke Dept. 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. July 27 and 28: Pre-conference tutorials. Fan et al. In such cases, it is necessary to address the impact of uncertainty during the planning process. Lectures on stochastic programming : modeling and theory / Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczynski. 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. EE364A — Stochastic Programming 16. 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 … The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. A two-day introductory series of Tutorials precedes the main conference to provide introduction to some of the central research areas in Stochastic Programming. 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. Stochastic Programming: Tutorial Part I W. Römisch Humboldt-University Berlin Institute of Mathematics 10099 Berlin, Germany ... 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