1.

Record Nr.

UNINA9910367242103321

Autore

Klein Haneveld Willem K

Titolo

Stochastic Programming : Modeling Decision Problems Under Uncertainty / / by Willem K. Klein Haneveld, Maarten H. van der Vlerk, Ward Romeijnders

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020

ISBN

3-030-29219-3

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (255 pages) : illustrations

Collana

Graduate Texts in Operations Research, , 2662-6012

Disciplina

519.7

Soggetti

Operations research

Decision making

Probabilities

Mathematical optimization

Economics

Operations Research/Decision Theory

Probability Theory and Stochastic Processes

Optimization

Economic Theory/Quantitative Economics/Mathematical Methods

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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

Sommario/riassunto

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.