Vaccine selection for COVID-19 by AHP and novel VIKOR hybrid approach with interval type-2 fuzzy sets

An Interval-valued intuitionistic Fuzzy group decision-making approach based on projection measurement extended VIKOR for patent quality evaluation

Patent quality assessment is a multi-attribute decision-making problem. The increasingly complex social environment makes group decision-making based on interval-valued intuitionistic fuzzy (IVIF) information more and more important. In group decision-making, considering using a comprehensive projection measure to calculate the similarity between two decision matrices is one of the important steps. In order to make up for the deficiencies of the classical projection measure and the existing normalized projection measure in measuring the similarity between two IVIF vectors and optimize the group decision-making performance of the VIKOR method, this study proposes: (1) a new definition of subtraction of IVIF numbers, which further enriches the theoretical basis of IVIF numbers; (2) a new normalized projection measure to measure the similarity between two IVIF matrices; (3) combined with the regret matrix to store group regret information, which enhances the comprehensive ability of the classical VIKOR technique, and then establishes a comprehensive GDM method for patent quality assessment. Finally, a specific patent quality assessment example in the existing literature is used for data experiment. Through static and dynamic experimental comparative analysis, the feasibility and effectiveness of the proposed method are illustrated, and it can almost produce a stable ranking. The research in this paper not only provides an effective group decision-making tool for patent quality assessment, but also provides theoretical support and practical guidance for fuzzy group decision-making problems in other fields.

Assessment of potential dominant factors for brownfield landscape regeneration: A case study in Xi'an, China

Rapid global urbanization has made brownfield reuse a vital issue for sustainable urban development. However, the regeneration of brownfield landscapes is a complex and lengthy process that requires a combination of factors to be considered. Their landscape regeneration must be planned and prioritized to utilize brownfield sites and achieve positive social benefits. Therefore, an urgent need must be established to establish an assessment framework and system for various types of brownfield landscape regeneration dominant factors to find different brownfield landscape regeneration dominant factors. This research developed an assessment model using the Analytic Hierarchy Process (AHP), covering five brownfield types: industrial, mining, military, transportation, and landfill in Xi'an, China. The potential assessment factors in three levels were analyzed for weighting to explore the dominant factors for the potential regeneration of brownfield landscapes in Xi'an. The results showed that, firstly, among the five first-level assessment factors, the physicality factor was the most important. Secondly, among the 16 second-level factors, the spatial and physical features of the visual landscape were the most critical. Finally, among the 40 three-level factors, spatial features were the primary factor. Therefore, the purpose of this research is to provide a specific assessment system and data analysis methods and ideas for the dominant factors of urban brownfield landscape regeneration in China and other regions based on the assessment framework with strong adaptability proposed by the AHP method, which can be flexibly adapted in the different areas and countries, to realize the sustainable development of cities in various regions.

Fuzzy APPSS: A novel method for quantifying COVID-19 impact in India under triangular spherical fuzzy environment

In the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique is needed to assess uncertainty in real-time complex situations. Therefore, an association between preference and performance with satisfactory score (APPSS) method is introduced as a fuzzy decision-making method that incorporates two components: preference and performance. This method focuses on demonstrating a connection between preference and performance with a satisfactory measure. Preference analysis evaluates the significance of criteria, while performance analysis assesses the effectiveness of each alternative based on these criteria. Additionally, the satisfactory measure ensures the reliability of the outcomes. The applicability of the proposed method is demonstrated by analyzing the impact of COVID-19 on different age groups in India across various categories. The proposed method employs triangular spherical fuzzy numbers (TSFN), which is a mathematical model that extends beyond conventional fuzzy numbers by incorporating both triangular and spherical characteristics. Furthermore, a new scoring function for TSFN is developed using the graded mean integration method. The analysis reveals that the age group between 60-69 is highly vulnerable to COVID-19. The robustness of these outcomes is verified through sensitivity and comparative analyses. The findings also assist policymakers in more effectively assessing potential future health complications.

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

How to ensure the normal production of industries in an uncertain emergency environment has aroused a lot of concern in society. Selecting the best emergency material suppliers using the multicriteria group decision making (MCGDM) method will ensure the normal production of industries in this environment. However, there are few studies in emergency environments that consider the impact of the decision order of decision makers (DMs) on the decision results. Therefore, in order to fill the research gap, we propose an extended MCGDM method, whose main steps include the following: Firstly, the DMs give their assessment of all alternatives. Secondly, we take the AHP method and entropy weight method to weight the criteria and the DMs. Thirdly, we take the intuitionistic fuzzy hybrid priority weight average (IFHPWA) operator we proposed to aggregate evaluation information and take the TOPSIS method to rank all the alternatives. Finally, the proposed method is applied in a case to prove its practicability and effectiveness. The proposed method considers the influence of the decision order of the DMs on the decision results, which improves the accuracy and efficiency of decision-making results.

Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates

BACKGROUND AND OBJECTIVE

Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions.

METHODS

Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate.

RESULTS

The results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario.

CONCLUSIONS

The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.