Robustness Analysis in Decision Aiding, Optimization, and Analytics

  • Michael Doumpos
  • Constantin Zopounidis
  • Evangelos Grigoroudis

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 241)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Risto Lahdelma, Pekka Salminen
    Pages 1-20
  3. David Ríos Insua, Fabrizio Ruggeri, Cesar Alfaro, Javier Gomez
    Pages 39-58
  4. Seçil Sözüer, Aurélie C. Thiele
    Pages 89-112
  5. André Chassein, Marc Goerigk
    Pages 145-170
  6. Christian Artigues, Jean-Charles Billaut, Azzedine Cheref, Nasser Mebarki, Zakaria Yahouni
    Pages 191-220
  7. Alexandros Nikas, Haris Doukas
    Pages 239-263
  8. Back Matter
    Pages 319-321

About this book


This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies.

Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a “big-data'” era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.


Analytics Bayesian Robustness Decision Aiding Decision Support Robust Optimization Robustness Analysis

Editors and affiliations

  • Michael Doumpos
    • 1
  • Constantin Zopounidis
    • 2
  • Evangelos Grigoroudis
    • 3
  1. 1.School of Production Engineering & MgmtTechnical University of CreteChaniaGreece
  2. 2.School of Production Engineering & MgmtTechnical University of CreteChaniaGreece
  3. 3.School of Production Engineering & MgmtTechnical University of CreteChaniaGreece

Bibliographic information