Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

Parents' acceptance attitudes towards the vaccination of children based on M-LSGDM approach in China: a cross-sectional study

OBJECTIVES

Ensuring that children receive timely vaccinations is paramount for preventing infectious diseases, and parental attitude plays a pivotal role in this process. This study addresses this gap in the existing literature by examining parental attitudes towards vaccinating their children.

DESIGN

A cross-sectional study.

METHODS

An online survey including parents' sociodemographic characteristics, risk perception and attitudes towards child vaccination towards COVID-19 was conducted. The modified large-scale group decision-making approach for practicality and binary logistic regression was used to identify the predictors influencing parents' decision-making.

RESULTS

Of the 1292 parents participated, 957 (74.1%) were willing to vaccinate their children, while 335 (25.9%) refused the vaccination. The study indicated that age, parental anxiety regarding child vaccination, concerns about the child's susceptibility to the disease, opinions towards vaccination benefits versus disadvantages, place of residence, average family income and children's health were significant predictors (p<0.05).

CONCLUSIONS

While most parents supported childhood vaccination, some opposed it. Addressing persistent barriers is crucial to ensure widespread vaccination and child well-being.

Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL

Based on previous evidence, the use of blockchain for improving Supply Chains (SCs) regarding humanitarian projects has received attention over the past five years. The present study is innovative in investigating crucial parameters affecting the using of Blockchain Technology (BT) in Humanitarian Supply Chains (HSCs). More precisely, this study emphasizes parameters that affect blockchain in the HSCs and presents a new fuzzy large-scale group decision-making trial and evaluation laboratory (fuzzy large-scale group-DEMATEL) approach to analyze the interdependence of contributing factors for using BT in HSCs. This method consists of two stages: (1) clustering the large-scale group-experts into small subgroups by their characteristics, and (2) identifying the key factors affecting BT in HSCs with a novel fuzzy large-scale group-DEMATEL approach. According to experts, in this study, among the 25 evaluated factors, disintermediation has been identified as the most important one, followed by anonymity and security. A closer look reveals that 13 and 12 factors have been "cause" and "effect" factors, respectively. Our research can be used to promote the effectiveness of using BT in HSCs, so as to promote the proper distribution of relief materials in practical disasters.

Exponential function-driven single-valued neutrosophic entropy and similarity measures and their applications to multi-attribute decision-making

Single-valued neutrosophic set is an important tool for describing fuzzy information and solving fuzzy decision problems. It is known that entropy can be applied to measure the degree of uncertainty of evaluation information and determine the important degree of objects, similarity is mainly used to capture the internal relationship of the evaluation objects. Therefore, single-valued neutrosophic entropy and single-valued neutrosophic similarity are two important topics in multi-attribute decision-making (MADM) problems. In this paper, some new single-valued neutrosophic entropy and similarity methods are first proposed to deal with uncertain and fuzzy decision problems with the help of exponential function. Then, the proofs of exponential entropy and exponential similarity measures fit the definition of single-valued neutrosophic similarity and single-valued neutrosophic entropy are presented. Moreover, we apply these two measure methods to cope with the MADM problems, then a new MADM method is provided. Finally, the developed MADM method is applied to the practical example of investment decision, and comparisons with other methods are conducted to show the advantages and rationality of our method.

Promoting the efficiency of scientific and technological innovation in regional industrial enterprises: Data-driven DEA-Malmquist evaluation model

With the increasing severity of the global energy crisis and environmental pollution, there is an urgent need to change the economic development model driven by certain factors and the investment scale and pursue science- and technology-driven innovative development. This study aims to improve the efficiency of scientific and technological innovation and promote the high-quality development of regional industrial enterprises. It constructs a data-driven DEA-Malmquist evaluation model to evaluate and optimize regional industrial enterprises’ scientific and technological innovation efficiency. First, we collect the panel data of regional industrial enterprises’ scientific and technological innovation input-output indexes. Second, we use the Pearson correlation coefficient method to identify and construct the evaluation index system of regional industrial enterprises’ scientific and technological innovation efficiency. Third, we build a DEA-Malmquist evaluation model to quantitatively evaluate regional industrial enterprises’ scientific and technological innovation efficiency from static and dynamic aspects. Finally, we verify the feasibility and effectiveness of the method using statistical data on scientific and technological innovation and development of Anhui Industrial Enterprises from 2011 to 2019 and put forth targeted countermeasures and suggestions. This study provides theoretical and methodological support for the sustainable development of industrial enterprises.

Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures.

A Group Emergency Decision-Making Method for Epidemic Prevention and Control Based on Probabilistic Hesitant Fuzzy Prospect Set Considering Quality of Information

Epidemics can bring huge impacts to economic operation and human health, a practical and effective emergency decision-making (EDM) method is of great significance to reduce all kinds of losses and slow the spread of epidemics. In the process of EDM, decision information is usually uncertain and vague, and the psychological behaviors and various perspectives of decision makers (DMs) should be considered. Hence, this paper develops a group emergency decision-making (GEDM) method under risk based on the probabilistic hesitant fuzzy set (PHFS) and cumulative prospect theory (CPT), in which probabilistic hesitant fuzzy prospect set (PHFPS) that combines PHFS and CPT is developed to portray the vagueness of decision information and psychologies of DMs. Moreover, experts’ creditability in evaluation criteria is generally different because of the differences of their own knowledge structures, practical experience, individual preference and so on. A formula is proposed to measure the quality of decision information provided by experts for revising the expert weights. In addition, the evaluation criteria supporting the GEDM of epidemics are given. Finally, the proposed method is demonstrated by an empirical case study of COVID-19, and the comparison analysis based on the rank-biased overlap model and the sensitivity analysis are conducted to the illustrate the validity of the proposed method.

An FGM decomposition-based fuzzy MCDM method for selecting smart technology applications to support mobile health care during and after the COVID-19 pandemic

In a fuzzy multicriteria decision-making (MCDM) problem, a decision maker may have differing viewpoints on the relative priorities of criteria. However, traditional methods merge these viewpoints into a single one, which leads to an unrepresentative decision-making result. Several recent methods identify the multiple viewpoints of a decision maker by decomposing the decision maker's fuzzy judgment matrix into several symmetric fuzzy subjudgment matrices, which is an inflexible strategy. To enhance flexibility, this study proposed a fuzzy geometric mean (FGM) decomposition-based fuzzy MCDM method in which FGM is applied to decompose a fuzzy judgment matrix into several fuzzy subjudgment matrices that can be asymmetric. These fuzzy subjudgment matrices are diverse and more consistent than the original fuzzy judgment matrix. The proposed methodology was applied to select the best choice from a group of smart technology applications for supporting mobile health care during and after the COVID-19 pandemic. According to the experimental results, the proposed methodology provided a novel approach to decomposing fuzzy judgment matrices and produced more diverse fuzzy subjudgment matrices. .

Evaluation of employee green behavior ability based on a fuzzy BWM-VIKOR approach

As a core resource of the company, employees play a major role to implement green management related behaviors in enterprises. Management department is also working hard to improve the ability of employees to perform these green behaviors for the company’s sustainable development capabilities. This study is the first effort that evaluation of effect factors of employee green behavior ability (EGBA) by intuitionistic fuzzy number-best worst method (IFN-BWM). To reach the study objective, a total of four criteria and twenty-seven sub-criteria for evaluation of EGBA are collected from the existing literatures. Subsequently, the PFN-VIKOR methodology (Pythagorean Fuzzy Number-Visekriterijumska Optimizacija I Kom-promisno Resenje) is proposed to rank EGBA levels. The results of this study show that employee self-efficacy and employee initiative in learning relevant green knowledge are important factors to enhance EGBA. Moreover, findings confirm that extended fuzzy semantic values and novel algorithm can accurately measure the decision makers’ mind and improve the accuracy of evaluation. This study also provides a framework for managers to evaluate their employee’ green behavior ability.

A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant Transformation

In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker’s fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.