We propose a risk-based stochastic VIKOR (RB-VIKOR) model that accounts for differences in the risk attitudes of the decision makers (DMs) when ranking stochastic alternatives. Our proposed RB-VIKOR model is designed to solve multi-criteria problems characterized by stochastic data and DMs categorized by their risk averse or risk seeking behavior. These differences in risk attitudes determine the subjective beliefs of the DMs regarding the evaluation of each alternative per decision criterion and the resulting rankings. We present a case study in the banking industry to illustrate how differences in the risk attitudes of the DMs condition the rankings obtained. Moreover, we compare our results with those derived from a stochastic super-efficiency data envelopment analysis (DEA) model to demonstrate the applicability and efficacy of RB-VIKOR. The proposed method has a considerable amount of potential applications to diverse research areas ranging from economics to knowledge based and decision support systems. © 2017 Elsevier Inc. All rights reserved.
An extended stochastic VIKOR model with decision maker's attitude towards risk / Tavana, M.; Di Caprio, D.; Santos-Arteaga, F. J.. - In: INFORMATION SCIENCES. - ISSN 0020-0255. - 432:(2018), pp. 301-318. [10.1016/j.ins.2017.12.019]
An extended stochastic VIKOR model with decision maker's attitude towards risk
Di Caprio D.;
2018-01-01
Abstract
We propose a risk-based stochastic VIKOR (RB-VIKOR) model that accounts for differences in the risk attitudes of the decision makers (DMs) when ranking stochastic alternatives. Our proposed RB-VIKOR model is designed to solve multi-criteria problems characterized by stochastic data and DMs categorized by their risk averse or risk seeking behavior. These differences in risk attitudes determine the subjective beliefs of the DMs regarding the evaluation of each alternative per decision criterion and the resulting rankings. We present a case study in the banking industry to illustrate how differences in the risk attitudes of the DMs condition the rankings obtained. Moreover, we compare our results with those derived from a stochastic super-efficiency data envelopment analysis (DEA) model to demonstrate the applicability and efficacy of RB-VIKOR. The proposed method has a considerable amount of potential applications to diverse research areas ranging from economics to knowledge based and decision support systems. © 2017 Elsevier Inc. All rights reserved.File | Dimensione | Formato | |
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