This paper focuses on employing the dynamic programming algorithm to solve the large-scale group decision making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fuzzy sets are employed to encode them. Then, new distance model and similarity model are respectively developed to measure the relationships between the interval type-2 fuzzy sets. After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision makers, respectively. Finally, a centroid-based ranking method is developed to compare and rank the alternatives, and two illustrative experiments are provided to illustrate the effectiveness of the proposed method. Comparisons and discussions are also conducted to verify its superiority.

Pythagorean fuzzy set (PFS) can provide more flexibility than intuitionistic fuzzy set (IFS) for handling uncertain information, and PFS has been increasingly used in multi-attribute decision making problems. This paper proposes a new multiattribute group decision making method based on Pythagorean uncertain linguistic variable Hamy mean (PULVHM) operator and VIKOR method. Firstly, we define operation rules and a new aggregation operator of Pythagorean uncertain linguistic variable (PULV) and explore some properties of the operator. Secondly, taking the decision makers' hesitation degree into account, a new score function is defined, and we further develop a new group decision making approach integrated with VIKOR method. Finally, an investment example is demonstrated to elaborate the validity of the proposed method. Sensibility analysis and comprehensive comparisons with another two methods are performed to show the stability and advantage of our method.

As a key technology to perform sustainable development in transportation, electric vehicles have been largely welcomed due to their advantages in energy savings and low carbon emission. The main step in promoting these vehicles is the selection of the appropriate electric vehicle charging station (EVCS) site. EVCS site selection is a laborious task because it involves a series of conflicting quantitative and qualitative criteria from several dimensions. The quantitative criteria are usually expressed by numerical data, while qualitative criteria are commonly represented by linguistic terms. Furthermore, the linguistic terms generated by different decision makers are usually defined on multi-granular linguistic term sets. In this paper, we present a new decision-making framework to select sustainable EVCS sites within the context of heterogeneous information and multi-granular linguistic terms. First, three information transformation mechanisms are defined to unify the heterogeneous information and multi-granular linguistic terms into interval-valued belief structures. Afterwards, shadowed sets theory is utilized to reflect the personalized individual semantics of linguistic terms. Then, with the aid of the evidential reasoning algorithm, a new information fusion method is proposed to generate the interval-valued expected utilities of alternatives. Subsequently, an improved minimax regret approach is developed to compare and rank the interval-valued expected utilities. The proposed decision-making framework is then implemented to solve a case study for EVCS site selection. Further analysis and comparisons with other methods are also conducted to show the applicability and feasibility of the current proposal.

This study presents a novel group Three-Way Decision (TWD) method that effectively addresses decision-making problems characterized by interval-valued uncertain information by integrating regret theory and the Consensus Reaching Process (CRP) model. The proposed method explores several key principles derived from regret theory to transform the loss functions into regret theory-based interval-valued loss functions. Additionally, improvements are made to the TOPSIS method for calculating conditional probabilities of alternatives under an interval-valued uncertain environment. Subsequently, several novel interval-valued TWD rules are established to classify alternatives into three predefined decision regions without the requirements for information transformation. Moreover, a personalized CRP model is developed based on the classification results, assisting experts with low consensus in revising their opinions to achieve the desired consensus. To assess the effectiveness of the proposed method, an illustrative example is presented, accompanied by comparative and sensitivity analyses that highlight its advantages and robustness. The results emphasize the innovative perspective and methodology provided by the proposed method for effectively addressing practical decision problems in uncertain and risk-laden environments.