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Modeling with Stochastic Programming (Springer Series in Operations Research and Financial Enginee)

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Modeling with Stochastic Programming (Springer Series in Operations Research and Financial Enginee) Cover

 

Synopses & Reviews

Publisher Comments:

This book is about modeling stochastic programs - models solved by optimization technology, whose solutions perform well under uncertainty. Major parts of the book are critical discussions about what different modeling paradigms actually mean and what they imply about the choices under consideration. Understanding why stochastic programs are needed, being able to formulate them, and finally, finding out what it is that makes solutions robust, can help find good solutions without actually solving the stochastic programs. Therefore, this book is much more than a book on how to build unsolvable models. Rather, it shows a way forward so that we can potentially benefit from a modeling framework. The book assumes the reader already has basic undergraduate knowledge of linear programming and probability, and some introduction to modeling from operations research, management science or something similar. Some facility with compiling and running programs in C++ is required to run the software examples.

Synopsis:

This book bridges theory and application of stochastic programming in operations research. It describes various methods of formulating stochastic optimization problems, and illustrates their advantages and disadvantages with examples and case studies.

Synopsis:

While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This text would be suitable as a

Table of Contents

Uncertainty in Optimization.-Modeling Feasibility and Dynamics.-Modeling the Objective Function.- Scenario tree generation, With Michal Kaut.-Service network design, With Arnt-Gunnar Lium and Teodor Gabriel Crainic.- A multi-dimensional newsboy problem with substitution, With Hajnalka Vaagen.- Stochastic Discount Factors.- Long Lead Time Production, With Aliza Heching.- References.- Index<

Product Details

ISBN:
9780387878164
Author:
King, Alan J.
Publisher:
Springer
Author:
Wallace, Stein W.
Subject:
Statistics
Subject:
Real Options Modeling
Subject:
Stochastic Discount Factors
Subject:
Stochastic programming formulation
Subject:
Uncertainty in Optimization
Subject:
Probability Theory and Stochastic Processes
Subject:
Operations Research/Decision Theory
Subject:
OPTIMIZATION
Subject:
Numerical analysis
Subject:
Mathematics-Computer
Subject:
Operation Research/Decision Theory
Copyright:
Edition Description:
2012
Series:
Springer Series in Operations Research and Financial Engineering
Series Volume:
1
Publication Date:
20120501
Binding:
HARDCOVER
Language:
English
Pages:
189
Dimensions:
235 x 155 mm

Related Subjects


Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Computer
Science and Mathematics » Mathematics » Foundations and Logic
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Introduction
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Probability Theory
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Modeling with Stochastic Programming (Springer Series in Operations Research and Financial Enginee) New Hardcover
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$77.50 In Stock
Product details 189 pages Springer - English 9780387878164 Reviews:
"Synopsis" by , This book bridges theory and application of stochastic programming in operations research. It describes various methods of formulating stochastic optimization problems, and illustrates their advantages and disadvantages with examples and case studies.
"Synopsis" by , While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This text would be suitable as a
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