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Stochastic programming = modeling de...

Klein Haneveld, Willem K.

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Stochastic programming = modeling decision problems under uncertainty /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic programming/ by Willem K. Klein Haneveld, Maarten H. van der Vlerk, Ward Romeijnders.
Reminder of title:	modeling decision problems under uncertainty /
Author:	Klein Haneveld, Willem K.
other author:	Vlerk, Maarten H. van der.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xii, 249 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Random Objective Functions -- Recourse Models -- Stochastic Mixed-integer Programming -- Chance Constraints -- Integrated Chance Constraints -- Assignments -- Case Studies.
Contained By:	Springer eBooks
Subject:	Stochastic programming - Textbooks. -
Online resource:	https://doi.org/10.1007/978-3-030-29219-5
ISBN:	9783030292195

Stochastic programming = modeling decision problems under uncertainty /
Klein Haneveld, Willem K.

Stochastic programmingmodeling decision problems under uncertainty /[electronic resource] :by Willem K. Klein Haneveld, Maarten H. van der Vlerk, Ward Romeijnders. - Cham :Springer International Publishing :2020. - xii, 249 p. :ill., digital ;24 cm. - Graduate texts in operations research,2662-6012. - Graduate texts in operations research..

Introduction -- Random Objective Functions -- Recourse Models -- Stochastic Mixed-integer Programming -- Chance Constraints -- Integrated Chance Constraints -- Assignments -- Case Studies.

This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book's closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.

ISBN: 9783030292195

Standard No.: 10.1007/978-3-030-29219-5doiSubjects--Topical Terms:

3444260
Stochastic programming
--Textbooks.

LC Class. No.: T57.79

Dewey Class. No.: 519.7

Stochastic programming = modeling decision problems under uncertainty /
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300 $a xii, 249 p. : $b ill., digital ; $c 24 cm.
490 1 $a Graduate texts in operations research, $x 2662-6012
505 0 $a Introduction -- Random Objective Functions -- Recourse Models -- Stochastic Mixed-integer Programming -- Chance Constraints -- Integrated Chance Constraints -- Assignments -- Case Studies.
520 $a This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book's closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.
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