Description: 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.
Review Quotes:
"As stochastic optimization problems can be solved only approximatively, the book presents the mathematical foundations for approximation methods as well as practical algorithms and examples for the generation and handling of scenario trees. ... The book, covering the current status in multistage stochastic optimization, can be recommended to readers interested in theoretical as well as in practical aspects of this field." (Kurt Marti, Mathematical Reviews, June, 2015)