Description

Book Synopsis

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.

Trade Review

“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)



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

Stochastic Programming: Modeling Decision Problems Under Uncertainty

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    A Paperback by Willem K. Klein Haneveld, Maarten H. van der Vlerk, Ward Romeijnders

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      Book details

      Publisher Springer Nature Switzerland AG
      Published 5 November 2020
      ISBN-13 9783030292218
      978-3030292218
      ISBN-10 3030292215
      Also in:
      Business, Finance & Law Operational research Mathematics Optimization Probability and statistics

      Description

      Book Synopsis

      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.

      Trade Review

      “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)



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

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