Titlebook: Robustness Analysis in Decision Aiding, Optimization, and Analytics; Michael Doumpos,Constantin Zopounidis,Evangelos Gr Book 2016 Springer

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Robustness for Adversarial Risk Analysis,om and depend on the actions of all participating agents. In this chapter, we outline a framework for robust analysis methods in Adversarial Risk Analysis. Our discussion focuses on security applications.

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From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-M broad area of decision-making under uncertainty. The objective of this chapter is therefore twofold. First, to examine the basic conceptual and modeling aspects of this ostensibly intuitive, yet controversial paradigm, so as to clarify some of the issues involved in its deployment in decision-makin

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The State of Robust Optimization,advancement of knowledge both with respect to the theory of robust optimization and application areas. From a theoretical standpoint, we describe novel findings in static and multi-stage decision making, the connection with stochastic optimization, distributional robustness and robust nonlinear opti

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Robust Discrete Optimization Under Discrete and Interval Uncertainty: A Survey,tions are discussed. Various robust concepts are presented, namely the traditional minmax (regret) approach with some of its recent extensions, and several two-stage concepts. A special attention is paid to the computational properties of the robust problems considered.

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Performance Analysis in Robust Optimization,ain optimization problems using the assignment problem and the knapsack problem as illustrative examples. As it is not immediately clear in practice which such robustness approach is suitable for the problem at hand, we present current approaches for evaluating and comparing robustness from the lite

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Robust-Soft Solutions in Linear Optimization Problems with Fuzzy Parameters, However, they were based on satisficing approaches so that the solutions do not maintain the optimality or suboptimality against the fluctuations in the coefficients. In this chapter, we describe a robust solution maintaining the suboptimality against the fluctuations in the coefficients. We formul

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How Robust is a Robust Policy? Comparing Alternative Robustness Metrics for Robust Decision-Making,conomic developments. The challenge is to develop robust policies, which perform well across all possible resolutions of the uncertainties. One approach for achieving this is to design a policy to be adapted over time in response to how the future actually unfolds. A key determinant for the efficacy

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