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Landfills are an effective way to dispose of waste and appropriate landfill sites can lessen environmental damage during waste treatment. Solid waste landfill site (SWLS) selection has received much attention in the area of multi-criteria decision-making in recent years. However, the uncertainty and complexity of the SWLS selection make it a significant challenge for decision makers (DMs). Since single-valued neutrosophic (SVN) sets have the great advantage of handling complex problems with uncertain and inconsistent information, this paper aims at offering a site planning strategy under the SVN environment. For the SWLS selection problem with interrelated factors, the Schweizer–Sklar power Bonferroni mean operator is first created, which not only considers the possible correlations among attributes but also reduces the adverse effects of anomalous assessment information on decision results. Then, a multi-criteria analysis framework based on the aggregation operator is proposed and then applied to a real-world SWLS selection. DMs can flexibly adjust the parameters in this model to achieve a preferred SWLS that integrates economic, environmental, and social perspectives. The consistent results obtained from the comparative analysis highlight its benefits for selecting proper SWLSs. [ABSTRACT FROM AUTHOR]

Single-valued neutrosophic sets can efficiently depict a great deal of imprecise, uncertain and discordant information. Hamy mean operator can consider the interrelationships among multiple integrated arguments and Schweizer-Sklar operations express great flexibility in the process of information aggregation. To give full consideration to these advantages, we merge the Hamy mean operator with the Schweizer-Sklar operations in single-valued neutrosophic environment, proposing a single-valued neutrosophic Schweizer-Sklar Hamy mean operator and a single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator. Besides, we illustrate some specific cases and attributes of the two operators. Moreover, based on the entropy weight method and the single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator, this paper presents a single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method to tackle multi-attribute decision making problems. At last, the method and other three existing methods are applied to solve a practical multi-attribute decision making problem, which validates the credibility and validity of the single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method by comparing the differences among them. [ABSTRACT FROM AUTHOR]

Single-valued neutrosophic (SVN) sets can successfully describe the uncertainty problems, and Schweizer-Sklar (SS) t-norm (TN) and t-conorm (TCN) can build the information aggregation process more flexible by a parameter. To fully consider the advantages of SVNS and SS operations, in this article, we extend the SS TN and TCN to single-valued neutrosophic numbers (SVNN) and propose the SS operational laws for SVNNs. Then, we merge the prioritized aggregation (PRA) operator with SS operations, and develop the single-valued neutrosophic Schweizer-Sklar prioritized weighted averaging (SVNSSPRWA) operator, single-valued neutrosophic Schweizer-Sklar prioritized ordered weighted averaging (SVNSSPROWA) operator, single-valued neutrosophic Schweizer-Sklar prioritized weighted geometric (SVNSSPRWG) operator, and single-valued neutrosophic Schweizer-Sklar prioritized ordered weighted geometric (SVNSSPROWG) operator. Moreover, we study some useful characteristics of these proposed aggregation operators (AOs) and propose two decision making models to deal with multiple-attribute decision making (MADM) problems under SVN information based on the SVNSSPRWA and SVNSSPRWG operators. Lastly, an illustrative example about talent introduction is given to testify the effectiveness of the developed methods. [ABSTRACT FROM AUTHOR]

Digital technology refers to any technology that uses digital signals or electronic data to process, store, and transmit information. Some examples of digital technologies include social media platforms, cloud computing, artificial intelligence, virtual and augmented reality, and blockchain technology. Digital technology has the potential to play a significant role in achieving sustainable development goals by providing solutions for a wide range of environmental, social, and economic challenges. In this manuscript, we investigate digital technology implementation under sustainable development and would find which area of sustainable development is most in need of digital technology. Further, we investigate the operational laws based on Schweizer–Sklar t-norm and t-conorm and originate aggregation operators based on these deduced operational laws under the environment of bipolar complex fuzzy set that is bipolar complex fuzzy Schweizer–Sklar power averaging, bipolar complex fuzzy Schweizer–Sklar power weighted averaging, bipolar complex fuzzy Schweizer–Sklar power geometric and bipolar complex fuzzy Schweizer–Sklar power weighted geometric operators and then we deduce techniques of decision-making utilizing these originated operators. Afterward, we tackle a numerical example related to the digital technology implementation under sustainable development by considering artificial data and finding the area of sustainable development which is most in need of digital technology. Moreover, we reveal the impact of one of the digital technologies that are artificial intelligence in the field of healthcare and study a numerical example by considering hypothetical data by employing the originated technique of decision-making. At the last, we do a comparison of the deduced operators with numerous current operators to reveal the superiority and benefits of the deduced operators. • Investigated digital technology implementation under sustainable development. • Investigated the role and impact of AI in the field of healthcare. • Determined how AI influences healthcare via decision-making techniques. • Novel Schweizer–Sklar aggregation operators for bipolar complex fuzzy sets are initiated. • Used defined operators to handle healthcare issues based on digital technology. • Comparative study is initiated to prove the value of proposed techniques. [ABSTRACT FROM AUTHOR]

The interval neutrosophic set (INS) can make it easier to articulate incomplete, indeterminate, and inconsistent information, and the Schweizer-Sklar (Sh-Sk) t-norm (tm) and t-conorm (tcm) can make the information aggregation process more flexible due to a variable parameter. To take full advantage of INS and Sh-Sk operations, in this article, we expanded the Sh-Sk and to IN numbers (INNs) in which the variable parameter takes values from , develop the Sh-Sk operational laws for INNs and discussed its desirable properties. After that, based on these newly developed operational laws, two types of generalized prioritized aggregation operators are established, the generalized IN Sh-Sk prioritized weighted averaging (INSh-SkPWA) operator and the generalized IN Sh-Sk prioritized weighted geometric (INSh-SkPWG) operator. Additionally, we swot a number of valuable characteristics of these intended aggregation operators (AGOs) and created two novel decision-making models to match with multiple-attribute decision-making (MADM) problems under IN information established on INSh-SkPWA and INSh-SkPRWG operators. Finally, an expressive example regarding evaluating the technological innovation capability for the high-tech enterprises is specified to confirm the efficacy of the intended models.

Schweizer⁻Sklar (SS) operation can make information aggregation more flexible, and the Muirhead mean (MM) operator can take into account the correlation between inputs by a variable parameter. Because traditional MM is only available for real numbers and single-valued neutrosophic set (SVNS) can better express incomplete and uncertain information in decision systems, in this paper, we applied MM operators to single-valued neutrosophic sets (SVNSs) and presented two new MM aggregation operators with the SS operation, i.e., a single-valued neutrosophic SS Muirhead mean (SVNSSMM) operator and a weighted single-valued neutrosophic SS MM (WSVNSSMM) operator. We listed some properties of them and some particular cases about various parameter values. We also proposed the multi-criteria decision-making method based on the WSVNSSMM operator in SVNS. At last, we illustrated the feasibility of this method using a numerical example of company investment.

The interval neutrosophic set (INS) can make it easier to articulate incomplete, indeterminate, and inconsistent information, and the Schweizer-Sklar (Sh-Sk) t-norm (tm) and t-conorm (tcm) can make the information aggregation process more flexible due to a variable parameter. To take full advantage of INS and Sh-Sk operations, in this article, we expanded the Sh-Sk and to IN numbers (INNs) in which the variable parameter takes values from [ ∞ − , 0) , develop the Sh-Sk operational laws for INNs and discussed its desirable properties. After that, based on these newly developed operational laws, two types of generalized prioritized aggregation operators are established, the generalized IN Sh-Sk prioritized weighted averaging (INSh-SkPWA) operator and the generalized IN Sh-Sk prioritized weighted geometric (INSh-SkPWG) operator. Additionally, we swot a number of valuable characteristics of these intended aggregation operators (AGOs) and created two novel decision-making models to match with multiple-attribute decision-making (MADM) problems under IN information established on INSh-SkPWA and INSh-SkPRWG operators. Finally, an expressive example regarding evaluating the technological innovation capability for the high-tech enterprises is specified to confirm the efficacy of the intended models. [ABSTRACT FROM AUTHOR]

Schweizer–Sklar (SS) operation can make information aggregation more flexible, and the Muirhead mean (MM) operator can take into account the correlation between inputs by a variable parameter. Because traditional MM is only available for real numbers and single-valued neutrosophic set (SVNS) can better express incomplete and uncertain information in decision systems, in this paper, we applied MM operators to single-valued neutrosophic sets (SVNSs) and presented two new MM aggregation operators with the SS operation, i.e., a single-valued neutrosophic SS Muirhead mean (SVNSSMM) operator and a weighted single-valued neutrosophic SS MM (WSVNSSMM) operator. We listed some properties of them and some particular cases about various parameter values. We also proposed the multi-criteria decision-making method based on the WSVNSSMM operator in SVNS. At last, we illustrated the feasibility of this method using a numerical example of company investment. [ABSTRACT FROM AUTHOR]