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

口味

Portfolio Optimization with Second-Order Stochastic Dominance Constraints and Portfolios Dominatingor instance, the mean-variance approach uses mean and covariance matrix of return of instruments of a portfolio. However this conventional approach ignores tails of return distribution, which may be quite important for the portfolio evaluation. This chapter considers the portfolio optimization probl

DEBT

Robust DEA Approaches to Performance Evaluation of Olive Oil Production Under Uncertainty,r to measure production efficiency of olive-growing farms, we first apply an imprecise DEA approach by taking into account optimistic and pessimistic perspectives on uncertainty realized in olive oil production yield. We then consider robust optimization based DEA under an uncertainty set where the

Ballerina

Original

Robust DEA Approaches to Performance Evaluation of Olive Oil Production Under Uncertainty,merical experiments reveal that the efficiency scores and efficiency discriminations dramatically depend on how the uncertainty is treated both in imprecise and robust DEA modeling. There exists a trade-off between the protection level and conservatism of the efficiency scores.

狂怒

SMAA in Robustness Analysis,chapter describes SMAA in robustness analysis using a real-life decision problem as an example. Basic robustness analysis is demonstrated with respect to uncertainty in criteria and preference measurements. Then the analysis is extended to consider also the structure of the decision model.

戰(zhàn)役

Data-Driven Robustness Analysis for Multicriteria Classification Problems Using Preference Disaggreization tools. The proposed measures enrich existing robust multicriteria approaches with additional information taken directly from the available data though an analytical approach. The properties and performance of the new robustness indicators are illustrated through their application to an example data set.

senile-dementia

From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-Me used in . and .. We thereby chart the journey of this paradigm from the field of . to that of modern ., highlighting its use in the latter, as a tool for dealing with both . and . robustness. We also look briefly at the relationship between probabilistic and worst-case-based robustness analysis.

violate

Robust Optimization Approaches to Single Period Portfolio Allocation Problem,iew robust approaches to deal with data uncertainty for a single-period portfolio allocation problem. We first introduce the main ideas of robust optimization using symmetric and asymmetric uncertainty sets where the uncertain asset returns belong to. We then focus on data driven and distributionally robust optimization approaches.

縮影

The State of Robust Optimization,mization. In terms of application areas, we consider inventory and logistics, finance, revenue management and health care. We conclude with guidelines for researchers interested in immunizing their problem against uncertainty.

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Performance Analysis in Robust Optimization,rature, and introduce the new concept of a scenario curve. Using the methods presented in this chapter, an easy guide is given to the decision maker to find, solve and compare the best robust optimization method for his purposes.