Stochastic Programming

Stochastic Programming

Author: Gerd Infanger

Publisher: Springer Science & Business Media

Published: 2010-11-10

Total Pages: 373

ISBN-13: 1441916423

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From the Preface... The preparation of this book started in 2004, when George B. Dantzig and I, following a long-standing invitation by Fred Hillier to contribute a volume to his International Series in Operations Research and Management Science, decided finally to go ahead with editing a volume on stochastic programming. The field of stochastic programming (also referred to as optimization under uncertainty or planning under uncertainty) had advanced significantly in the last two decades, both theoretically and in practice. George Dantzig and I felt that it would be valuable to showcase some of these advances and to present what one might call the state-of- the-art of the field to a broader audience. We invited researchers whom we considered to be leading experts in various specialties of the field, including a few representatives of promising developments in the making, to write a chapter for the volume. Unfortunately, to the great loss of all of us, George Dantzig passed away on May 13, 2005. Encouraged by many colleagues, I decided to continue with the book and edit it as a volume dedicated to George Dantzig. Management Science published in 2005 a special volume featuring the “Ten most Influential Papers of the first 50 Years of Management Science.” George Dantzig’s original 1955 stochastic programming paper, “Linear Programming under Uncertainty,” was featured among these ten. Hearing about this, George Dantzig suggested that his 1955 paper be the first chapter of this book. The vision expressed in that paper gives an important scientific and historical perspective to the book. Gerd Infanger


Quantitative Methods

Quantitative Methods

Author: Paolo Brandimarte

Publisher: John Wiley & Sons

Published: 2012-01-03

Total Pages: 812

ISBN-13: 111802348X

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An accessible introduction to the essential quantitative methods for making valuable business decisions Quantitative methods-research techniques used to analyze quantitative data-enable professionals to organize and understand numbers and, in turn, to make good decisions. Quantitative Methods: An Introduction for Business Management presents the application of quantitative mathematical modeling to decision making in a business management context and emphasizes not only the role of data in drawing conclusions, but also the pitfalls of undiscerning reliance of software packages that implement standard statistical procedures. With hands-on applications and explanations that are accessible to readers at various levels, the book successfully outlines the necessary tools to make smart and successful business decisions. Progressing from beginner to more advanced material at an easy-to-follow pace, the author utilizes motivating examples throughout to aid readers interested in decision making and also provides critical remarks, intuitive traps, and counterexamples when appropriate. The book begins with a discussion of motivations and foundations related to the topic, with introductory presentations of concepts from calculus to linear algebra. Next, the core ideas of quantitative methods are presented in chapters that explore introductory topics in probability, descriptive and inferential statistics, linear regression, and a discussion of time series that includes both classical topics and more challenging models. The author also discusses linear programming models and decision making under risk as well as less standard topics in the field such as game theory and Bayesian statistics. Finally, the book concludes with a focus on selected tools from multivariate statistics, including advanced regression models and data reduction methods such as principal component analysis, factor analysis, and cluster analysis. The book promotes the importance of an analytical approach, particularly when dealing with a complex system where multiple individuals are involved and have conflicting incentives. A related website features Microsoft Excel® workbooks and MATLAB® scripts to illustrate concepts as well as additional exercises with solutions. Quantitative Methods is an excellent book for courses on the topic at the graduate level. The book also serves as an authoritative reference and self-study guide for financial and business professionals, as well as readers looking to reinforce their analytical skills.


Choice-set Demand in Revenue Management: Unconstraining, Forecasting and Optimization

Choice-set Demand in Revenue Management: Unconstraining, Forecasting and Optimization

Author: Alwin Haensel

Publisher: Alwin Haensel

Published: 2012-06-27

Total Pages: 189

ISBN-13: 9081909509

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Focus on Profit! Maximize your revenue and profit by understanding and considering your customers’ buying behavior. How price sensitive are your customers? What are their preferences? How strong are the competitor influences or cannibalization effects in your own product portfolio? These questions must be answered analytically, in order to obtain a quantitative understanding of the customers’ choice process and hence a clear picture of the demand in the market. We propose the notion of choice-sets as our model for the customers’ preferences and buying decisions. The unconstraining is the related process which extracts demand information with choice behavior from product sales data. Once, we obtained the information of current and past demand data, the immediate next step is the demand forecasting. Finally, with an accurate estimate of the future demand, we continue with the optimization process, to derive optimal sales controls and pricing actions which maximize the overall revenue or profit.


Introduction to Stochastic Programming

Introduction to Stochastic Programming

Author: John R. Birge

Publisher: Springer Science & Business Media

Published: 2011-06-15

Total Pages: 500

ISBN-13: 1461402379

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The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now 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 aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on 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. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: "The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)


The Theory and Practice of Revenue Management

The Theory and Practice of Revenue Management

Author: Kalyan T. Talluri

Publisher: Springer Science & Business Media

Published: 2006-02-21

Total Pages: 731

ISBN-13: 0387273913

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Revenue management (RM) has emerged as one of the most important new business practices in recent times. This book is the first comprehensive reference book to be published in the field of RM. It unifies the field, drawing from industry sources as well as relevant research from disparate disciplines, as well as documenting industry practices and implementation details. Successful hardcover version published in April 2004.


Dynamic Fleet Management

Dynamic Fleet Management

Author: Vasileios S. Zeimpekis

Publisher: Springer Science & Business Media

Published: 2007-10-05

Total Pages: 249

ISBN-13: 0387717226

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This book focuses on real time management of distribution systems, integrating the latest results in system design, algorithm development and system implementation to capture the state-of-the art research and application trends. The book important topics such as goods dispatching, couriers, rescue and repair services, taxi cab services, and more. The book includes real-life case studies that describe the solution to actual distribution problems by combining systemic and algorithmic approaches.


Capacity Options for Revenue Management

Capacity Options for Revenue Management

Author: Rolf Hellermann

Publisher: Springer Science & Business Media

Published: 2006-10-07

Total Pages: 208

ISBN-13: 3540344209

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This book proposes capacity options as a flexible alternative air cargo contract type, and illustrates how capacity can be priced through option contracts. The analysis is accomplished by means of an analytical multivariate optimization model under price and demand uncertainty. A case study using data from a leading German carrier illustrates the financial potential. Finally, the author shows how capacity-option contracts integrate into the context of air cargo revenue management.


Multistage Stochastic Optimization

Multistage Stochastic Optimization

Author: Georg Ch. Pflug

Publisher: Springer

Published: 2014-11-12

Total Pages: 309

ISBN-13: 3319088432

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Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book.