Jiapeng Liu

A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty

A new group decision-making approach is developed to address a multiple criteria sorting problem with uncertainty. The uncertainty in this paper refers to imprecise evaluations of alternatives with respect to the considered criteria. The belief structure and the evidential reasoning approach are employed to represent and aggregate the uncertain evaluations. In our approach, the preference information elicited from a group of decision makers is composed of the assignment examples of some reference alternatives. The disaggregation–aggregation paradigm is utilized to infer compatible preference models from these assignment examples. To help the group reach an agreement on the assignment of alternatives, we propose a consensus-reaching process. In this process, a consensus degree is defined to measure the agreement among the decision makers’ opinions. When the decision makers are not satisfied with the consensus degree, possible solutions are explored to help them adjust assignment examples in order to improve the consensus level. If the consensus degree arrives at a satisfactory level, a linear program is built to determine the collective assignment of alternatives. The application of the proposed approach to a customer satisfaction analysis is presented at the end of the paper.

A social ties-based approach for group decision-making problems with incomplete additive preference relations

With the rapid growth of Web 2.0 technology, a new paradigm has been developed that allows many users to participate in decision-making processes within online social networks. The social information (i.e., social ties and social influence) of the members that is stored in online social networks provides a new perspective for investigating group decision-making (GDM) problems. In this paper, a new interactive GDM approach, based on online social networks, is proposed to address a ranking problem with incomplete additive preference relations (IAPRs). This approach incorporates the strength of social ties and social influence calculated by social network analysis methods regarding the decision-making process. After decision makers (DMs) provide IAPRs, a searching algorithm is developed to identify the optimal preference information transfer path from DMs to the decision supporters who can provide the corresponding preference information. Next, a linear programming model is constructed to complete the missing preference values of the IAPRs. The main features of the linear programming model include its ability to account for other DMs preference information and to maintain consistency. To help the group reach an agreement on the ranking of alternatives, a consensus reaching process is proposed. The strength of social ties and social influence are used to calculate the acceptable adjustment coefficients for DMs in the feedback mechanism. Finally, an illustrative example and further discussion demonstrate the validity of the proposed approach.

A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples

We propose a novel approach to address a multiple criteria sorting (MCS) problem with an imbalanced set of assignment examples. The approach employs a piecewise-linear additive value function as the preference model and adopts the disaggregation–aggregation paradigm to infer a sorting model from provided assignment examples on a set of reference alternatives. We utilize a hierarchical clustering algorithm and several linear programming models to identify reference alternatives that are active to develop the sorting model, so that inactive ones are eliminated from the whole set of reference alternatives. Then, in order to construct a balanced set of assignment examples, a balancing algorithm is proposed to balance active reference alternatives across categories. Finally, the sorting model is obtained by minimizing the sum of violations between values of active reference alternatives and corresponding category thresholds. Furthermore, the performance of the proposed approach is investigated on a hypothetical problem and several real data sets. The experimental results show that our approach is efficient to address the MCS problem with an imbalanced set of assignment examples.