Synopses & Reviews
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.
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.
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<>