In this manuscript, spherical fuzzy set (SFS) and T-spherical fuzzy set (TSFS) are discussed, which are two generalizations of fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PFS) and picture fuzzy set (PFS). As TSFS is more capable of processing and expressing unknown information in unknown environment, it is widely used in various areas. However, how to accurately measure the distance between TSFSs is still an unsolved problem. This manuscript discusses some limitations of the existing divergence measures and the problems that the existing divergence measures cannot be applied to the information provided in the TSFSs environment by some numerical examples. Therefore, a new divergence measure under TSFSs structure is proposed by utilizing the advantages of Jensen-Shannon divergence, which is called TSFSJS distance. This TSFSJS distance not only satisfies the distance measurement axiom, but also can better distinguish the difference between TSFSs than other distance measures. More importantly, this TSFSJS distance can avoid counter-intuitive results through the argument of some numerical results in the paper. The proposed approach can deal with more types of uncertain information as demonstrated by establishing a comparative study.

The green supplier selection has gradually become a hot topic around the world. To achieve the purpose of optimal decision-making, it is of great importance to process the imprecise and ambiguous information that may exist in the evaluation and selection of green suppliers. In the present article, first, converting the imprecise and ambiguous information into interval-valued Pythagorean fuzzy numbers (IVPFNs) through the Likert scale. Then, using the interval-valued Pythagorean fuzzy weighted average (IVPFWA) operator and multiplicative theory to introduce the IVPFNs into the non-radial SE-BCC model of Data Envelopment Analysis (DEA), we developed the DEA model in the interval-valued Pythagorean fuzzy environment. Later, ranking all decision-making units (DMUs) to be evaluated and selecting the best of them. Moreover, The proposed model can not only deal with the case where the quantitative indicators are interval-valued numbers, but also solve the problem of qualitative analysis in the evaluation process. Finally, a numerical example of green suppliers selection is provided to demonstrate the feasibility and usefulness of the model.

As third-party logistics providers occupy an increasingly important position in the operation strategies of various enterprises, choosing a partner that suits them and can bring benefits to them has become an important decision-making issue for each enterprise. A lot of studies have been done with classical fuzzy methods, but there are relatively few studies that consider information reliability. The emergence of Z-numbers makes up for this deficiency, including the restriction on the evaluation object and the corresponding degree of confidence. In this article, making decisions based on Z-numbers can better transform language values into fuzzy numbers. By integrating the existing Multi-Attributive Border Approximation area Comparison (MABAC) method and Z-numbers, a new method for the selection of third-party logistics providers is provided. Finally, the feasibility and effectiveness of the approach are verified by comparing with the classical Multi Criteria Decision Making methods. The Z-number is a relatively new concept that can flexibly represent the confidence of information, which will be a relatively important research direction in the future.

The purpose of this paper is to study the multi-attribute decision-making problem under the fuzzy picture environment. First, a method to compare the pros and cons of picture fuzzy numbers (PFNs) is introduced in this paper. Second, the correlation coefficient and standard deviation (CCSD) method is used to determine the attribute weight information under the picture fuzzy environment regardless of whether the attribute weight information is partially unknown or completely unknown. Third, the ARAS and VIKOR methods are extended to the picture fuzzy environment, and the proposed PFNs comparison rules are also applied in the PFS-ARAS and PFS-VIKOR methods. Fourth, the problem of green supplier selection in a picture-ambiguous environment is solved by the method proposed in this paper. Finally, the method proposed in this paper is compared with some methods and the results are analyzed.

Technology selection is an important part for enterprises sustainable development. The best technologies could create significant competitive advantages for an enterprise to realize its profit growth and capability improvement, and then realize its sustainable development. However, due to the complexity of technology selection, decision makers are faced with a difficult task, therefore, to select the best technologies, we introduce the evaluation based on distance from average solution (EDAS) method to aggregate ultimate cross-efficiency scores. By calculating the positive distance from average solution (PDA) and the negative distance from average solution (NDA), we can get the appraisal scores (AS) to rank for each Rated decision making unit (DMU). Finally, an example of technology selection is illustrated to examine the validity of the proposed method.