Data, Models, and Decisions: The Fundamentals of Management Science

This book represents a departure from existing textbooks. Rather than covering methodology, the book introduces decision support systems through real world applications, and uses spreadsheets to model and solve problems. It uses management science techniques (statistics, simulation, probabilistic modeling and optimization), but only as tools to facilitate problem solving. The book embraces George Dantzig's dictum as its premise: "The final test of any theory is its capacity to solve the problems which originated it." 
 The book is used in the core MBA program at MIT's Sloan School of Management for the class titled: "Data, Models and Decisions". It is used in over ten universities in both core and elective classes, including Stanford University, Chicago's graduate school of Business, Babson College, among several others. 
 The book contains many real business cases from the authors' consulting experience. Instructor resources are available for faculty who have adopted the book for their course by contacting the authors directly @ dbertsim@mit.edu, rfreund@mit.edu )

Business & Money

Bertsimas, Dimitris

Management & Leadership

Dynamic Ideas LLC

A decision support system (DSS) is an intelligent information system that uses data, models it, processes or analyzes it using problem-specific methodologies, and assists the user in the decision-making process through a graphical user interface (GUI). This is a comprehensive book that describes how to build decision support systems using the Excel spreadsheet framework and the VBA programming language. Risk Solver Platform for Education, an Excel Add-In, is used for optimization and simulation. This book illustrates complete decision support development applications through several case studies arising in operations research, industrial engineering, management, and business administration. 
Changes in the 2nd Edition 
Part I - This book features Excel 2010. In Chapter 2 we give an overview of the Ribbon, Quick Access Toolbar and File Menu tab which is a new addition to Excel's interface. In this Chapter we also discuss the new conditional formatting features of Excel. In Chapter 3 we discuss the new and improved charting interface of Excel and introduce Sparklines. In Chapter 4 we discuss some of the improvements made on Excel's function library. In Chapter 6 we present some of the improved capabilities for working with pivot tables and introduce Slicers. In Chapters 8 and 9 we use Risk Solver Platform for Education to model and solve optimization and simulation problems. In Chapter 10 we present the new Official Excel Table tools and discuss its capabilities to organize and manipulate data. 
Part II - In Chapters 19 and 20 we discuss how to use Object Oriented API with Risk Solver Platform to modify and solve optimization and simulation models using VBA commands. 
Part III - A number of the case studies (such as, Birthday Simulation, Portfolio Management and Optimization, Single and Multi-Server Queuing Simulation, etc.), are updated to use Risk Solver Platform for optimization and simulation.

Developing Spreadsheet-Based Decision Support Systems

Sandra D. Eksioglu

Operations Strategy: Principles and Practice provides a unified framework for operations strategy. The book shows how to tailor the operational system to maximize value and competitive advantage. Conceptual thinking and financial optimization yield guidelines for implementation. This dual emphasis on principles and practice is reflected by analytical models that are illustrated with detailed examples and a dozen case studies of real business situations. What's new in the second edition?This second edition contains changes along various dimensions. First, ProfessorAllon has joined this ongoing book project as second author. In 2010, Gadstarted teaching the MBA elective course, Operations Strategy , that Jan began designingand teaching in 1998. Second, while the first edition followed a "leadingstrategy," in the sense that it contained material that hadn't been taught yet, thissecond edition reflects how our MBA elective course has evolved over the last 7years. It thus contains "class-room tested content." These two changes led to anew chapter 2; majorly revised chapters 1, 3, 4, 5, 6, and 7 and appendix A; andminor revisions of the remaining chapters.

OPERATIONS STRATEGY

Jan A. Van Mieghem

Gad Allon

This book is an introductory textbook in Probability, written from the viewpoint of Applied Mathematics. Under that perspective, theoretical rigor is not denigrated, but the book puts a high premium on offering an intuitive overview of key theorems as well as clear evidence of their usefulness. Beyond teaching specific methods, the book strives to create real comfort with the underlying notions of probabilistic thinking. Grasping such ideas is essential if they are to be used with competence and confidence. A primary objective of the book is to demonstrate that Probability and Statistics are, more than anything, just crystallizations of common sense. The reader should come away from each lesson understanding not only how a result was obtained but also why the outcome is plausible. Praises for Applied Probability "Offering much more than math, this winning book teachers you to think with probability. Developing the intuition required to understand and recast real world situations in probabilistic terms is a crucial skill in itself, and I can think of no other text that does more in this regard" - Prof. Edward Kaplan, Yale University A unique and wonderful book, with examples and exercises that are brilliant. They reinforce basic concepts, help students to adapt tools and techniques to new situations (no 'cookie cutter' problems here), and reveal how probability and statistics pervade our lives." - Prof. Robert Shumsky, Dartmouth College About the Author Arnold I. Barnett is George Eastman Professor of Management Science and Professor of Statistics at MIT's Sloan School of Management. He has received the President's Award and Expository Writing Award from INFORMS, and the President's Citation from the Flight Safety Foundation for "truly outstanding contributions with respect to safety." At MIT, he has been honored for outstanding teaching of Probability and Statistics.

Applied Probability: Models and Intuition

Arnold I. Barnett

This introductory textbook in Statistics is written from the viewpoint of Applied Mathematics. Under that perspective, theoretical rigor is not denigrated, but the book puts a high premium on offering an intuitive overview of key methods as well as clear evidence of their usefulness. Beyond teaching particular techniques, the book strives to create real comfort with the underlying notions of statistical thinking. Grasping such ideas is essential if they are to be used with competence and confidence. Consistent with the theme of Applied Statistics, the book contains hundreds of worked examples, analyses of actual data, and practice exercises that serve as key components of the pedagogy. For example, we will explore with data which couples are the best candidates for in vitro fertilization, whether temperatures in New York City have been rising in a matter consistent with climate change, whether the Space Shuttle Challenger disaster was all but inevitable, when bystanders are least likely to help people in trouble, and what statistical evidence is considered compelling in job-discrimination case. Praises on the book: "A patient, humorous and experienced voice presents applied statistics in an intuitive, engaging and mathematically honest way" - Prof. Susan Martonosi, Harvey Mudd College "A unique approach.... Delightful.....an uncanny ability to explain intuitively even the most opaque concepts" - Prof. Amedeo Odoni, MIT About the Author Arnold I. Barnett is George Eastman Professor of Management Science and Professor of Statistics at MIT's Sloan School of Management. He has received the President's Award and Expository Writing Award from INFORMS, and the President's Citation from the Flight Safety Foundation for "truly outstanding contributions with respect to safety." At MIT, he has been honored by students 13 times for outstanding teaching of Probability and Statistics.

Applied Statistics: Models and Intuition

The Analytics Edge provides a unified, insightful, modern and entertaining treatment of analytics. The book covers the science of using data to build models, improve decisions, and ultimately add value to institutions and individuals. The philosophical underpinnings of the book are that real world problems are usually complex and often will defined; they do not come with labels, meaning that they are not necessarily regression problems or optimization problems. The only objective reality is data, which itself may be incomplete and of questionable quality, and the role of models is to facilitate the solution of real world problems. Problems and data play a leading role in this book, while models play an essential but supporting role. This is in contrast with the vast majority of books and classes available today, in which methods play the leading role. Dimitris Bertsimas is the Boeing Professor of Operations Research and the co-director of the Operations Research Center at the Massachusetts Institute of Technology. He is a member of the National Academy of Engineering, recipient of numerous research awards and co-founder of six Analytics companies. Allison K. O'Hair is a Lecturer in Operations, Information and Technology at the Stanford Graduate School of Business. She has a Ph.D. in Operations Research from MIT, and is a former Lecturer at the MIT Sloan School of Management. William R. Pulleyblank is Professor of Operations Research at the United States Military Academy, West Point. Previously he was Vice President of Business Optimization in IBM and before that led the BlueGene supercomputing project in IBM Research. He is a member of the National Academy of Engineering and has served on a broad range of business and government advisory boards.

The Analytics Edge

Dimitris Bertsimas

Allison O'Hair

William Pulleyblank

The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be optimal in minutes/hours, and outperform classical heuristic approaches in out-of-sample experiments. 
 Structure of the book: 
 Part I covers robust, sparse, nonlinear, holistic regression and extensions.
 Part II contains optimal classification and regression trees. 
 Part III outlines prescriptive ML methods. 
Part IV shows the power of optimization over randomization in design of experiments, exceptional responders, stable regression and the bootstrap. 
Part V describes unsupervised methods in ML: optimal missing data imputation and interpretable clustering. 
Part VI develops matrix ML methods: sparse PCA, sparse inverse covariance estimation, factor analysis, matrix and tensor completion .
 Part VII demonstrates how ML leads to interpretable optimization. 
Philosophical principles of the book: Interpretability is materially important in the real world. Practical tractability not polynomial solvability leads to real world impact. 
NP-hardness is an opportunity not an obstacle. 
 ML is inherently linked to optimization not probability theory. Data represent an objective reality; models only exist in our imagination. 
Optimization has a significant edge over randomization .
 The ultimate objective in the real world is prescription, not prediction.

Machine Learning Under a Modern Optimization Lens

This book provides an original treatment of robust (RO) and adaptive robust optimization (ARO) based on over twenty years of research by each of the authors.\nStructure of the Book\nPart I describes linear RO and the underlying uncertainty sets. 
Part II treats modeling, exact and approximate algorithms for ARO. 
Part III introduces nonlinear RO for concave uncertainty. Part IV outlines nonlinear RO for concave uncertainty. 
Part V discusses the theory of distributional RO and ARO. 
Part VI contains a variety of RO and ARO applications including queueing theory, auction design, option pricing and energy unit commitment. 
ORIGINAL CHARACTERISTICS OF THE BOOK: 
Emphasis on modeling in RO and ARO. 
Integrated treatment of RO and ARO and of nonlinear RO for concave and convex uncertainty, as opposed to robust conic optimization. 
Interplay of probability theory to select parameters in RO and of optimization for tractability.

Robust and Adaptive Optimization

Dick Den Hertog

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