Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error-based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.

Qualitative portfolio selection approach is a suitable technique for obtaining an optimal portfolio when quantitative data are unavailable and traditional portfolio models are ineffective. However, few studies focus on this issue. This study addresses the lack of research by defining the score-hesitation trade-off rule and introducing the intuitionistic fuzzy set (IFS), based on which an intuitionistic fuzzy portfolio selection (IFPS) model is proposed. The IFS is introduced because of its comprehensive consideration of preference and nonpreference, and is used to represent qualitatively evaluated information from investors and experts. Furthermore, an intuitionistic fuzzy investment scenario is established and a trisection approach is designed to distinguish three types of risk investors, based on which three corresponding IFPS models are constructed. After this, a portfolio selection process under the intuitionistic fuzzy environment is provided, and a simple example is given to show the application of the process. In addition, the investment opportunities and efficient frontier of the IFPS model are investigated to demonstrate the effectiveness of the proposed portfolio selection model. Finally, an example of calculating optimal investment ratios and selecting an optimal portfolio for four newly listed stocks in China is provided to demonstrate the feasibility and practicability of the proposed approaches.

On the basis of the hesitant fuzzy membership, this study proposes the extended intuitionistic fuzzy set (EIFS) and the extended intuitionistic fuzzy number (EIFN) to synthesize the characters of the intuitionistic fuzzy set and the hesitant fuzzy set. We further develop two simplified and applied EIFSs, namely the credible EIFS (C-EIFS) and the possible EIFS (P-EIFS), to comprehensively mine the hesitant fuzzy membership information and to avoid the logical difficulty of simultaneously providing the membership and non-membership in each EIFS or EIFN. Then we investigate the foundations of C-EIFS and P-EIFS, including their expressions, operations, functions, differences, and selection rules. The corresponding aggregation operators are also proposed, and the calculation and relationships of these operators are proven. The prominent properties of C-EIFNs and P-EIFNs are focused on the boundary and average values, respectively; that is, the C-EIFN tends to aggregate the extreme information, whereas the P-EIFN prefers aggregating complete information. Therefore, applying them to decision making with risk preference is suitable, and two risk preference investment cases are provided to demonstrate the applications of these concepts and approaches.