Introduction to Stochastic Programming

by John R. Birge
Introduction to Stochastic Programming
Author: John R. Birge
Publisher: Springer Science & Business Media
Total Pages: 427
Release: 2006-04-06
Genre: Mathematics
ISBN: 0387226184
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This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.

Stochastic Linear Programming

by Peter Kall
Stochastic Linear Programming
Author: Peter Kall
Publisher: Springer Science & Business Media
Total Pages: 439
Release: 2010-11-02
Genre: Mathematics
ISBN: 1441977295
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This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. ... The presentation includes geometric interpretation, linear programming duality, and the simplex method in its primal and dual forms. ... The authors have made an effort to collect ... the most useful recent ideas and algorithms in this area. ... A guide to the existing software is included as well." (Darinka Dentcheva, Mathematical Reviews, Issue 2006 c) "This is a graduate text in optimisation whose main emphasis is in stochastic programming. The book is clearly written. ... This is a good book for providing mathematicians, economists and engineers with an almost complete start up information for working in the field. I heartily welcome its publication. ... It is evident that this book will constitute an obligatory reference source for the specialists of the field." (Carlos Narciso Bouza Herrera, Zentralblatt MATH, Vol. 1104 (6), 2007)

Stochastic Decomposition

by Julia L. Higle
Stochastic Decomposition
Author: Julia L. Higle
Publisher: Springer Science & Business Media
Total Pages: 237
Release: 2013-11-27
Genre: Mathematics
ISBN: 1461541158
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Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.

Stochastic Linear Programming

by P. Kall
Stochastic Linear Programming
Author: P. Kall
Publisher: Springer Science & Business Media
Total Pages: 103
Release: 2012-12-06
Genre: Business & Economics
ISBN: 3642662528
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Todaymanyeconomists, engineers and mathematicians are familiar with linear programming and are able to apply it. This is owing to the following facts: during the last 25 years efficient methods have been developed; at the same time sufficient computer capacity became available; finally, in many different fields, linear programs have turned out to be appropriate models for solving practical problems. However, to apply the theory and the methods of linear programming, it is required that the data determining a linear program be fixed known numbers. This condition is not fulfilled in many practical situations, e. g. when the data are demands, technological coefficients, available capacities, cost rates and so on. It may happen that such data are random variables. In this case, it seems to be common practice to replace these random variables by their mean values and solve the resulting linear program. By 1960 various authors had already recog nized that this approach is unsound: between 1955 and 1960 there were such papers as "Linear Programming under Uncertainty", "Stochastic Linear Pro gramming with Applications to Agricultural Economics", "Chance Constrained Programming", "Inequalities for Stochastic Linear Programming Problems" and "An Approach to Linear Programming under Uncertainty".

Stochastic Programming

by Willem K. Klein Haneveld
Stochastic Programming
Author: Willem K. Klein Haneveld
Publisher: Springer Nature
Total Pages: 255
Release: 2019-10-24
Genre: Business & Economics
ISBN: 3030292193
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This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.

Lectures on Stochastic Programming

by Alexander Shapiro
Lectures on Stochastic Programming
Author: Alexander Shapiro
Publisher: SIAM
Total Pages: 447
Release: 2009-01-01
Genre: Mathematics
ISBN: 0898718759
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Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.

Stochastic Linear Programming

by Peter Kall
Stochastic Linear Programming
Author: Peter Kall
Publisher: Springer Science & Business Media
Total Pages: 416
Release: 2005
Genre: Business & Economics
ISBN: 9780387233857
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CONTENIDO: Basic - Linear Programming Prerequisites - Nonlinear Programming Prerequisites - Single-Stage SLP models - Models involving probability functions - Quantile functions, Value at Risk - Models based on expectation - Models built with deviation measures - Modeling risk and opportunity - Risk measures - Multi-stage SLP models - The general SLP with recourse - The two-stage SLP - The multi-stage SLP - Algorithms - Single-stage models with separate probability functions - Single-stage models with joint probability functions - Single-stage models based on expectation - Single-stage models involving VaR - Single-stage models with deviation measures - Two-stage recourse models - Multistage recourse models - Modeling systems for SLP.

Stochastic Programming

by RUSZCZYNSK
Stochastic Programming
Author: RUSZCZYNSK
Publisher: North Holland
Total Pages: 708
Release: 2003-10-09
Genre: Business & Economics
ISBN:
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Brings together leading in the most important sub-fields of stochastic programming to present a rigourous overview of basic models, methods and applications of stochastic programming. The text is intended for researchers, students, engineers and economists, who encounter in their work optimization problems involving uncertainty.

Applications of Stochastic Programming

by Stein W. Wallace
Applications of Stochastic Programming
Author: Stein W. Wallace
Publisher: SIAM
Total Pages: 724
Release: 2005-01-01
Genre: Mathematics
ISBN: 9780898718799
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Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.