Modeling with Stochastic Programming

Alan J. King

ISBN 10: 3031545524 / ISBN 13: 9783031545528

Published by Springer, Springer, 2025

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This is an updated version of what is still the only text to address basic questions about how tomodel 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 representrandom phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models.This text would be suitable as astand-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.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 and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.

Seller Inventory # 9783031545528

Bibliographic Details

• Title: Modeling with Stochastic Programming
• Publisher: Springer, Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 3031545524
• ISBN 13: 9783031545528
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 235W x 155H x 13D millimeters 235 millimeters width by 155 millimeters height by 13 millimeters depth
• Weight: 341 grams

About this title

Synopsis

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.

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 and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.

About the Author

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 and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.