Stochastic Programming: Modeling Decision Problems Under Uncertainty

ISBN-13: 9783030292188 / Angielski / Twarda / 2019 / 249 str.

Willem K. Klein Haneveld; Maarten H. Van Der Vlerk; Ward Romeijnders

Recenzje

"The book is well written. The book will be of interest to mathematicians, engineers, economics and especially graduate students." (I. M. Stancu-Minasian, zbMATH 1446.90118, 2020)

Spis treści

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

O autorze

Wim Klein Haneveld is Emeritus Professor in the Department of Operations at the University of Groningen. He is one of the pioneers of Stochastic Programming. He developed the Stochastic Programming course for graduate students at the University of Groningen and has taught this course for many years.

Maarten van der Vlerk was Professor in the Department of Operations at the University of Groningen. He was an expert in Stochastic Integer Programming. For many years he was lecturer of the Stochastic Programming course in Groningen and a PhD course on Stochastic Programming at the LNMB (the Dutch Network on the Mathematics of Operations Research).

Ward Romeijnders is Assistant Professor in the Department of Operations at the University of Groningen. He is an expert in Stochastic Integer Programming. He is the current lecturer of the Stochastic Programming courses in Groningen and at the LNMB. 

Blurb

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.