A multicriteria decision making methodology based on two-dimensional uncertainty by hesitant Z-fuzzy linguistic terms with an application for blockchain risk evaluation

Blockchain knowledge selection under the trapezoidal fermatean fuzzy number

Blockchain knowledge signifies a useful fundamental knowledge to safeguard faith in transboundary transmittals for main banks and financial institutions. In the study of group decision-making, the most important issue is how to coordinate opinions from different blockchains to reach a compromise under uncertainty. To tackle uncertainties surrounding multi-attribute group decision-making (MAGDM) problems in real-life scenes, we introduce a trapezoidal fermatean fuzzy set which generalizes trapezoidal fuzzy sets and fermatean fuzzy sets. The trapezoidal fermatean fuzzy model enables the degrees of membership, abstention, and non-membership to be expressed by linguistic terms. We define the operational laws of trapezoidal fermatean fuzzy numbers, and Einstein aggregation operator based on the trapezoidal fermatean fuzzy number. This makes it more flexible and descriptive to model the attitudes of Blockchain knowledge in MAGDM applications. Since multi-input arguments are interconnected and Blockchain knowledge has a lot of options perception, we also define the TOPSIS technique to facilitate the fusion of trapezoidal fermatean fuzzy information. With the aid of the trapezoidal fermatean fuzzy-TOPSIS technique, the main goal of this research is to present a general MAGDM framework by integrating the step with the complex proportional assessment. A trapezoidal fermatean positive ideal solution always wants the maximum value of the benefit criteria and the minimum value of the cost criteria. On the other hand, the trapezoidal fermatean negative ideal solution always wants the maximum value of the cost criteria and the minimum value of the benefit criteria. An integrated trapezoidal fermatean fuzzy-TOPSIS framework is established. In the proposed decision framework, the trapezoidal fermatean fuzzy-TOPSIS method is utilized to identify the subjective weights of decision attributes, and the trapezoidal fermatean fuzzy-TOPSIS approach is used to rank alternatives. Lastly, a case study concerning blockchain knowledge assessment is presented to demonstrate that the suggested scheme is feasible and effective. Furthermore, sensitivity and comparison analyses are conducted to show the robustness and superiority of the proposed method.

Investigation of the pharmaceutical warehouse locations under COVID-19-A case study for Duzce, Turkey

Pharmaceutical warehouses are among the centers that play a critical role in the delivery of medicines from the producers to the consumers. Especially with the new drugs and vaccines added during the pandemic period to the supply chain, the importance of the regions they are located in has increased critically. Since the selection of pharmaceutical warehouse location is a strategic decision, it should be handled in detail and a comprehensive analysis should be made for the location selection process. Considering all these, in this study, a real-case application by taking the problem of selecting the best location for a pharmaceutical warehouse is carried out for a city that can be seen as critical in drug distribution in Turkey. For this aim, two effective multi-criteria decision-making (MCDM) methodologies, namely Analytic Hierarchy Process (AHP) and Evaluation based on Distance from Average Solution (EDAS), are integrated under spherical fuzzy environment to reflect fuzziness and indeterminacy better in the decision-making process and the pharmaceutical warehouse location selection problem is discussed by the proposed fuzzy integrated methodology for the first time. Finally, the best region is found for the pharmaceutical warehouse and the results are discussed under the determined criteria. A detailed robustness analysis is also conducted to measure the validity, sensibility and effectiveness of the proposed methodology. With this study, it can be claimed that literature has initiated to be revealed for the pharmaceutical warehouse location problem and a guide has been put forward for those who are willing to study this area.

Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain

This study proposes a primary node election method based on probabilistic linguistic term set (PLTS) for the practical Byzantine fault tolerance (PBFT) consensus mechanism to effectively enhance the efficiency of reaching consensus. Specifically, a novel concept of the probabilistic linguistic term set with a confidence interval (PLTS-CI) is presented to express the uncertain complex voting information of nodes during primary node election. Then, a novel score function based on the exponential semantic value and confidence approximation value for the PLTS-CI, called Score-ESCA, is used to solve the problems of comparing different nodes with various voting attitudes. This method helps select the node with the highest score by utilizing complex decision attitudes, making it an accurate primary node election solution. Furthermore, the feasibility of our proposed method is proved by both theoretical analysis and experimental evaluations.

Blockchain Development Services Provider Assessment Model for a Logistics Organizations

As the global market develops along with technological advances, especially the opening of markets in developing and underdeveloped countries, the logistics industry is considered by managers to be a useful tool to link different functions of corporate strategy. Logistics is the process of planning, implementing and controlling the movement of goods or information that are related to raw materials (inputs) and final products (outputs) from the point of origin to the point of consumption in order to meet customer requirements. The logistics industry brings about many positive effects, not only for the national economy but also for creating equal business opportunities for businesses of all economic sectors. However, large companies in the logistics industry still depend on EDI or APIS to exchange secure authentication data, which increase the security of operations within the industry, but this is the cause of a lot of confusion with serious consequences on the supply chain when it is too dependent on these systems. To minimize the risks and optimize the problems mentioned above, blockchain technology could help the logistics industry to operate optimally. In an era when everything is digitized, from personal information to financial transactions, choosing a system with high reliability and security becomes extremely important. Currently, there are many blockchain development services providers and it is essential to choose the investment in this technology that suits the characteristics and purposes of the logistics organization in question. This study aimed to propose a two-stage fuzzy multicriteria decision-making model for the assessment of blockchain development services providers for logistics organizations. The hybrid MCDM model was evaluated through a real-world case study at a logistics organization. The contribution of this work is the provision of useful guidelines for the evaluation and selection of blockchain technology services providers for logistics companies.

Estimating Driver Behavior Measures Related to Traffic Safety by Investigating 2-Dimensional Uncertain Linguistic Data—A Pythagorean Fuzzy Analytic Hierarchy Process Approach

Human behavior has been estimated as a factor too uncertain and complex to investigate road safety issues. By utilizing recent expansions of ordinary fuzzy sets, experts in the field have intended to handle the vagueness of human behavior in sustainable transport systems by using linguistic terms for assessment. Pythagorean Fuzzy sets (PFSs) are considered a superior method that has been developed for multi-criteria decision-making (MCDM), which enables assigning of both membership and non-membership functions in a large domain area. A novel Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) is performed to assess and prioritize critical driver behavior criteria designed into a hierarchical model based on data gathered from observed driver groups in Budapest city. Accordingly, based on the aggregated weights, the criterion ‘lapses’ is prioritized as the most critical factor connected to road safety. The criterion ‘disobey speed limits’ is found to be the least critical factor, followed by ‘disobey overtaking rules’ as the second least. For a comparative analysis, the case of dependent criteria has been considered. Pythagorean Fuzzy DEMATEL method has been applied to determine dependencies between the criteria. Through the dependencies, a network of criteria has been constructed and the Pythagorean Fuzzy Analytic Network Process (ANP) conducted to interpret the results. Moreover, sensitivity analyses have been carried out to examine its robustness by applying different case scenarios.