Robustness analysis in decision aiding, optimization, and analytics

Table of Content:

1.SMAA in Robustness Analysis
2.Data-driven Robustness Analysis for Multicriteria Classification Problems Using Preference Disaggregation Approaches
3.Robustness for Adversarial Risk Analysis
4.From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-Making
5.The State of Robust Optimization
6.Robust Discrete Optimization under Discrete and Interval Uncertainty
7.A Survey
8.Performance Analysis in Robust Optimization
9.Robust-Soft Solutions in Linear Optimization Problems with Fuzzy Parameters
10.Robust Machine Scheduling Based on Group of Permutable Jobs
11.How Robust is a Robust Policy? Comparing Alternative Robustness Metrics for Robust Decision-making
12.Developing Robust Climate Policies: A Fuzzy Cognitive Map Approach
13.Robust Optimization Approaches to Single Period Portfolio Allocation Problem
14.Portfolio Optimization with Second-Order Stochastic Dominance Constraints and Portfolios Dominating Indices
15.Robust DEA Approaches to Performance Evaluation of Olive Oil Production under Uncertainty.

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.

http://www.springer.com/us/book/9783319331195