Modeling with Stochastic Programming

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 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.

The first and only book discussing how to model stochastic programs Mostly non-technical and focuses on the concepts Written by two of the key international researchers Includes supplementary material: sn.pub/extras

Inhalt

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