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 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 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. Alan King is a Research Staff Member at IBM's Thomas J.
Watson Research Center in New York. Stein W. Wallace is a Professor of Operational Research at Lancaster University Management School in England.
| ISBN: | 9781489992123 |
| Publication date: | 17th July 2014 |
| Author: | Alan J King, Stein W Wallace |
| Publisher: | Springer an imprint of Springer New York |
| Format: | Paperback |
| Pagination: | 176 pages |
| Series: | Springer Series in Operations Research and Financial Engineering |
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 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 issues are.
Hardback, Paperback. Not Available.
Modeling With Stochastic Programming was written by Alan J King, Stein W Wallace and published by Springer an imprint of Springer New York
Modeling With Stochastic Programming has 176 pages
Yes it is part of Springer Series in Operations Research and Financial Engineering series