Modeling with stochastic programming
- 2nd
- Cham Springer 2024
- xviii, 202 p.
- Springer Series in Operations Research and Financial Engineering (ORFE) .
Table of contents: Front Matter Pages i-xviii Download chapter PDF Uncertainty in Optimization Alan J. King, Stein W. Wallace Pages 1-35 Information Structures and Feasibility Alan J. King, Stein W. Wallace Pages 37-53 Modeling the Objective Function Alan J. King, Stein W. Wallace Pages 55-75 Scenario Tree Generation Alan J. King, Stein W. Wallace Pages 77-113 High-Dimensional Dependent Randomness Alan J. King, Stein W. Wallace Pages 115-122 Multistage Models Alan J. King, Stein W. Wallace Pages 123-155 Service Network Design Alan J. King, Stein W. Wallace Pages 157-176 A Multi-dimensional Newsboy Problem with Substitution Alan J. King, Stein W. Wallace Pages 177-192
This is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models.
This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are.
The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty