Modeling with stochastic programming
King, Alan J.
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
[https://link.springer.com/book/10.1007/978-3-031-54550-4]
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
(https://link.springer.com/book/10.1007/978-3-031-54550-4)
9783031545498
Probability theory
Numerical analysis
519.7 / KIN
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
[https://link.springer.com/book/10.1007/978-3-031-54550-4]
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
(https://link.springer.com/book/10.1007/978-3-031-54550-4)
9783031545498
Probability theory
Numerical analysis
519.7 / KIN