An Emergency Local Group Decision-Making Model Based on Variable Precision Rough Set

Risks evaluation is critical for the success of IT offshore outsourcing. Based on fuzzy group decisionmaking (FGDM) and variable precision fuzzy rough set (VPFRS), this article proposes a new integrated model, variable precision fuzzy rough group decision-making (VPFRGDM), to evaluate the risk in IT offshore outsourcing. This model can improve the capability to handle potential errors fairness and efficiency of risk evaluation, and is verified by a numerical case.

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Group Decision-Making Model Based on Rough Set and Evidence Theory

2010 International Conference on Computational and Information Sciences ◽

10.1109/iccis.2010.170 ◽

2010 ◽

Author(s):

Ruixia Yan ◽

Hualing Liu

Keyword(s):

Decision Making ◽

Rough Set ◽

Group Decision Making ◽

Group Decision ◽

Evidence Theory ◽

Model Based ◽

Decision Making Model

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Dynamic information aggregation decision-making methods based on variable precision rough set and grey clustering

Grey Systems Theory and Application ◽

10.1108/gs-04-2014-0010 ◽

2014 ◽

Vol 4 (2) ◽

pp. 347-361 ◽

Cited By ~ 2

Author(s):

Yong Liu ◽

Huan-huan Zhao

Keyword(s):

Decision Making ◽

Rough Set ◽

Information Aggregation ◽

Dynamic Decision Making ◽

Dynamic Information ◽

Content Type ◽

Variable Precision ◽

Model Based ◽

Variable Precision Rough Set ◽

Decision Making Model

Purpose – The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set. Design/methodology/approach – To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model. Findings – The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules. Research limitations/implications – The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers. Originality/value – The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

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Group Decision Model Based on Ordered Weighted Distance to Aid Decisions on Logistics

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488518500125 ◽

2018 ◽

Vol 26 (02) ◽

pp. 233-254 ◽

Cited By ~ 5

Author(s):

Jose Leao E Silva Filho ◽

Danielle Costa Morais

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Decision Model ◽

Group Decision ◽

Decision Rules ◽

Decision Makers ◽

Weighted Distance ◽

Model Based ◽

Decision Making Model

This paper presents a group decision-making model using a distance aggregator based on Ordered Weighted Distance (OWD) which offers a solution that can reduce disagreement between decision makers (DMs). This paper discusses decision rules and sets out measures to evaluate compensatory effects that have a bearing on DMs’ opinions. The model uses formulations of distances to reveal the differences in opinion among DMs and discusses the meanings of distance and the information presented by each DM. Finally, a case study of a logistics problem is used to illustrate how the model is applied.

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