Einstein Hybrid Structure of q-Rung Orthopair Fuzzy Soft Set and Its Application for Diagnosis of Waterborne Infectious Disease

This research is devoted to diagnosing water-borne infectious diseases caused by floods employing a novel diagnosis approach,the Einstein hybrid structure of q-rung orthopair fuzzy soft set.This approach integrates parts of fuzzy logic and soft set theory to develop a robust alternative for disease detection in stressful situations,especially in areas affected by floods.Compared to the traditional intuitionistic fuzzy soft set and Pythagorean fuzzy soft set,the q-rung orthopair fuzzy soft set(q-ROFSS)adequately incorporates unclear and indeterminate facts.The major objective of this investigation is to formulate the q-rung orthopair fuzzy soft Einstein hybrid weighted average(q-ROFSEHWA)operator and its specific characteristics.Moreover,our stated operator is implementing intelligentmulti-criteria group decision-making(MCGDM)methodology.Floods are severe natural catastrophes that raise the risk of diseases and epidemics,particularly those caused by contaminants in the water,such as gastrointestinal diseases,respiratory infections,vector-borne diseases,skin infections,and water-borne parasites.The designed MCGDM strategy tackles the prevalence of certain conditions in flood-affected patients.A comparative investigation determined that the suggested method for detecting water-borne infectious disease due to floods is more effective and productive than conventional methods because of its logical structure.

Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

Hybrid q-Rung Orthopair Fuzzy Sets Based CoCoSo Model for Floating Offshore Wind Farm Site Selection in Norway

Unlocking offshore wind farms’high energy generation potential requires a comprehensive multi-disciplinary analysis that consists of intensive technical,economic,logistical,and environmental investigations.Offshore wind energy projects have high investment volumes that make it essential to conduct extensive site selection to ensure feasible investment decisions that reduce the potential financial risks.Depending on the scenario and circumstances,a ranking of alternative offshore wind energy projects helps to prioritise the investment decisions.Decisionmaking algorithms based on expert knowledge can support the prioritisation and thus alleviate the work load for investment decisions in the future.The case study considered here is to find the best site for a floating offshore wind farm in Norway from four pre-selected alternatives:Utsira Nord,Stadthavet,Froyabanken,and Trana Vest.We propose a hybrid decisionmaking model as a combined compromised solution(CoCoSo)based on the q-rung orthopair fuzzy sets(q-ROFSs)including the weighted q-rung orthopair fuzzy Hamacher average(Wq-ROFHA)and the weighted q-rung orthopair fuzzy Hamacher geometric mean(Wq-ROFHGM)operators.In this model,the q-ROFSs based full consistency method(FUCOM)is introduced as a new methodology to determine the weights of the decision criteria.The results of the proposed model show that the best site among the investigated four alternatives is A1:Utsira Nord.A sensitivity analysis has verified the stability of the proposed decision-making model.

Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

Cubic q-Rung Orthopair Fuzzy Linguistic Set and Their Application to Multiattribute Decision-making with Muirhead Mean Operator

The objective of this paper is to present a new concept,named cubic q-rung orthopair fuzzy linguistic set(Cq-ROFLS),to quantify the uncertainty in the information.The proposed Cq-ROFLS is a qualitative form of cubic q-rung orthopair fuzzy set,where membership degrees and nonmembership degrees are represented in terms of linguistic variables.The basic notions of Cq-ROFLS have been introduced and study their basic operations and properties.Furthermore,to aggregate the different pairs of preferences,we introduce the Cq-ROFL Muirhead mean-(MM),weighted MM-,dual MM-based operators.The major advantage of considering the MM is that it considers the interrelationship between more than two arguments at a time.On the other hand,the Cq-ROFLS has the ability to describe the qualitative information in terms of linguistic variables.Several properties and relation of the derived operators are argued.In addition,we also investigate multiattribute decision-making problems under the Cq-ROFLS environment and illustrate with a numerical example.Finally,the effectiveness and advantages of the work are established by comparing with other methods.

Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment

Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment,the evaluation information of alternatives given by decision makers is often fuzzy and uncertain.As a generalization of intuitionistic fuzzy set(IFSs)and Pythagoras fuzzy set(PFSs),q-rung orthopair fuzzy set(q-ROFS)is more suitable for expressing fuzzy and uncertain information.But,in actual multiple attribute decision making(MADM)problems,the weights of DMs and attributes are always completely unknown or partly known,to date,the maximizing deviation method is a good tool to deal with such issues.Thus,combine the q-ROFS and conventional maximizing deviation method,we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper.Firstly,we briefly introduce the basic concept of q-rung orthopair fuzzy sets(q-ROFSs)and q-rung interval-valued orthopair fuzzy sets(q-RIVOFSs).Then,combine the maximizing deviation method with q-rung orthopair fuzzy information,we establish two new decision making models.On this basis,the proposed models are applied to MADM problems with q-rung orthopair fuzzy information.Compared with existing methods,the effectiveness and superiority of the new model are analyzed.This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers(q-ROFNs)and whose attributes are incomplete.

Medical Waste Treatment Station Selection Based on Linguistic q-Rung Orthopair Fuzzy Numbers

During the COVID-19 outbreak,the use of single-use medical supplies increased significantly.It is essential to select suitable sites for establishing medical waste treatment stations.It is a big challenge to solve the medical waste treatment station selection problem due to some conflicting factors.This paper proposes a multi-attribute decision-making(MADM)method based on the partitioned Maclaurin symmetric mean(PMSM)operator.For the medical waste treatment station selection problem,the factors or attributes(these two terms can be interchanged.)in the same clusters are closely related,and the attributes in different clusters have no relationships.The partitioned Maclaurin symmetric mean function(PMSMF)can handle these complex attribute relationships.Hence,we extend the PMSM operator to process the linguistic q-rung orthopair fuzzy numbers(Lq-ROFNs)and propose the linguistic q-rung orthopair fuzzy partitioned Maclaurin symmetric mean(Lq-ROFPMSM)operator and its weighted form(Lq-ROFWPMSM).To reduce the negative impact of unreasonable data on the final output results,we propose the linguistic q-rung orthopair fuzzy partitioned dual Maclaurin symmetric mean(Lq-ROFPDMSM)operator and its weighted form(Lq-ROFWPDMSM).We also discuss the characteristics and typical examples of the above operators.A novel MADM method uses the Lq-ROFWPMSM operator and the Lq-ROFWPDMSM operator to solve the medical waste treatment station selection problem.Finally,the usability and superiority of the proposed method are verified by comparing it with previous methods.

MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment

As the generalization of intuitionistic fuzzy set(IFS) and Pythagorean fuzzy set(PFS),the q-rung orthopair fuzzy set(q-ROFS) has emerged as a more meaningful and effective tool to solve multiple attribute group decision making(MAGDM) problems in management and scientific domains.The MABAC(multi-attributive border approximation area comparison) model,which handles the complex and uncertain decision making issues by computing the distance between each alternative and the bored approximation area(BAA),has been investigated by an increasing number of researchers more recent years.In our article,consider the conventional MABAC model and some fundamental theories of q-rung orthopair fuzzy set(q-ROFS),we shall introduce the q-rung orthopair fuzzy MABAC model to solve MADM problems.at first,we briefly review some basic theories related to q-ROFS and conventional MABAC model.Furthermore,the q-rung orthopair fuzzy MABAC model is built and the decision making steps are described.In the end,An actual MADM application has been given to testify this new model and some comparisons between this novel MABAC modeL and two q-ROFNs aggregation operators are provided to further demonstrate the merits of the q-rung orthopair fuzzy MABAC model.

Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

With the frequent occurrences of emergency events,emergency decision making(EDM)plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times.It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time,since inappropriate decisions may result in enormous economic losses and social disorder.To handle emergency effectively and quickly,this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough(q-ROPR)set.A novel list of q-ROFR aggregation information,detailed description of the fundamental characteristics of the developed aggregation operators and the q-ROFR entropy measure that determine the unknown weight information of decision makers as well as the criteria weights are specified.Further an algorithm is given to tackle the uncertain scenario in emergency to give reliable and reasonable emergency decisions.By using proposed list of q-ROFR aggregation information all emergency alternatives are ranked to get the optimal one.Besides this,the q-ROFR entropy measure method is used to determine criteria and experts’weights objectively in the EDM process.Finally,through an illustrative example of COVID-19 analysis is compared with existing EDM methods.The results verify the effectiveness and practicability of the proposed methodology.

Assessment of the Solid Waste Disposal Method during COVID-19 Period Using the ELECTRE Ⅲ Method in an Interval-Valued q-Rung Orthopair Fuzzy Approach

As the quantity of garbage created every day rises,solid waste management has become the world’s most important issue.As a result,improper solid waste disposal and major sanitary issues develop,which are only detected after they have become dangerous.Due to the system’s lockdown during the COVID-19 pandemic,this scenario became much more uncertain.We are at the stage to develop and execute effective waste management procedures,as well as long-term policies and forward-thinking programmes that can work even in the most adverse of scenarios.We incorporate major solid waste(organic and inorganic solid wastes)approaches that actually perform well in normal cases by reducing waste and environmental disasters;however,in such an uncertain scenario like the COVID-19 pandemic,the project automatically allows for a larger number of criteria,all of which are dealt with using fuzzy Multi-Criteria Group Decision Making(MCGDM)methods.The ELECTRE Ⅲ(ELimination Et Choice Translating REality-Ⅲ)approach,which is a novel decision-making strategy for determining the best way to dispose and reduce garbage by combining traditional ELECTRE Ⅲ with an interval-valued q-rung orthopair fuzzy set(IVq-ROFS),is described in detail in this article.To confirm the efficacy of the recommended model,a numerical explanation is provided,as well as sensitivity and comparative analyses.Obviously,the findings encourage decision-makers in authorities to deliberate about the proposals before creating solid waste management policies.

Analysis of crowdfunding platforms for microgrid project investors via a q-rung orthopair fuzzy hybrid decision-making approach

Effective crowdfunding platforms positively contribute toward improving micro-grid energy management systems.Therefore,a comprehensive analysis is required to understand the key factors responsible for success in crowdfunding systems by considering various criteria.This study evaluates crowdfunding platforms for microgrid project investments.In this context,a novel fuzzy decision-making model that includes two different stages is proposed.First,the selected criteria for the crowdfunding platforms for microgrid project investments are evaluated.Second,alternatives,regarding the microgrid project investments,are ranked.In this process,a multi-stepwise weight assessment ratio analysis(M-SWARA)approach based on q-rung orthopair fuzzy sets(q-ROFSs)is considered.Intuitionistic and Pythagorean fuzzy sets are also used in the calculation process to make a comparative evaluation.Similarly,a sensitivity analysis of the ranking alternatives is also conducted with 12 different q values.All the results are rather similar;thus,the findings are reliable.Another model is also created for this purpose with the help of the decision-making trial and evaluation laboratory(DEMA-TEL)and the technique for order preference by similarity to ideal solution methodologies to check the performance of the proposed model.It is defined that by considering the q-ROF DEMATEL weights,the ranking results vary for different cases.The proposed model with a M-SWARA is more reliable than the model created via the DEMATEL method.This situation provides information regarding the superiority of the model proposed in this study.It is concluded that security is the most important factor in crowdfunding platforms for smart-grid project investors.Additionally,solar panels and energy storage systems/batteries are the most significant alternatives for microgrid project investors.Necessary measures should be taken to forestall the risk of fraud that may occur on this platform.Therefore,the website to be established must be secure against possible hacking attacks.Another important conclusion of this study is that solar panels should be preliminarily developed to increase the effectiveness of microgrid systems.

基于一种新的q-rung orthopair模糊交叉熵的属性约简算法

熵是刻画模糊集不确定性程度的一种重要手段。为了反映q-rung orthopair模糊集中隶属度与非隶属度力量对比所产生的模糊性,首先提出相关的得分函数。针对目前大多数q-rung orthopair模糊集的相似性度量的不足,提出了更符合人们直觉的q-rung orthopair模糊集交叉熵。目前对q-rung orthopair模糊信息系统的属性约简研究相对较少,通过性质讨论和理论证明,发现这种交叉熵可以较好地应用于q-rung orthopair模糊信息系统的属性约简,设计了相关的属性约简算法,并通过实例说明了这种算法的合理性。其次,给出了将普通信息系统转换为q-rung orthopair模糊信息系统的方法,最后通过计算UCI中多个数据库,验证了所提属性约简算法的合理性和有效性,为q-rung orthopair模糊信息系统数据预处理提供了新的思路。

基于q-rung orthopair模糊相似测度的多属性决策方法

本文将模糊相似测度推广到q-rung orthopair模糊环境中,定义了基于隶属度、非隶属度与犹豫度的q-rung orthopair模糊集的两种相似测度形式并给出其性质。最后,我们提出了基于q-rung orthopair模糊集相似测度的医疗诊断的解决方法,证明该测度的可行性与实用性。

Multi-attribute group decision-making method with linguistic q-rung orthopair fuzzy information based on bi-direction Choquet integral

Purpose-The purpose of this paper is to develop the linguistic q-rung orthopair fuzzy set(LqROFS)information VIKOR method based on the bi-direction Choquet integral(BDCI),taking into account the correlation between information.The method can enrich the existing studies related to LqROFS information and better solve the problem of MAGDM problem.Design/methodology/approach-Since applying Choquet integral(CI)depict information interaction is a common operation in MAGDM.However,the traditional CI has some limitations.The unidirectional alignment may affect the MAGDM results.Therefore,a LqROFS-VIKOR method based on BDCI is proposed,where BDCI is used to aggregate the decision matrix.Furthermore,it is not reasonable to apply exact numbers to express the similarity between two qualitative data.Then a new method of defining similarity using linguistics is proposed.The similarity is used to calculate attribute weights.Findings-The validity and potential application of MAGMD method with linguistic q-rung orthopair fuzzy information based on BDCI are demonstrated in a numerical examples study.Originality/value-According to the study of available literature,the current research on LqROFS is incomplete.The existing studies of both similarity and aggregate operators have certain shortcomings.The definition of similarity proposed in this paper is more in line with reality.And compared with the existing methods,the BDCI-based aggregate operator can describe the interaction between information more reasonably.Based on this VIKOR method based on BDCI under the LqROFS environment can better select the alternative.

融合三支决策与GRA-Orthopair模糊集的知识匹配方法研究

为缓解用户“知识迷向”问题,提升知识应用及创新效益,提出了融合三支决策与GRA-Orthopair模糊集的知识匹配方法。首先基于CRITIC-熵权法进行权重配置,并融入博弈论方法增强客观赋权实效;而后,采用FCM聚类算法横向压缩匹配空间;进而,借鉴三支决策思想实施预分类,同时考虑知识间的关联度,构建基于灰色关联度的Orthopair模糊相似度模型,并据此计算用户需求与案例知识间的视图相似度,从而确定匹配结果。实验结果表明,该算法的知识匹配精度及效率优于传统匹配算法,提升了匹配效果。

基于区间Orthopair模糊距离测度的VIKOR多属性决策方法

针对决策信息为区间Orthopair模糊数的多属性决策问题,提出一种基于新的模糊距离测度的VIKOR多属性决策方法.提出一种新的区间Orthopair模糊距离测度,研究它的优良性质,包括非负性、单调性、自反性、交换律和三角不等式性质,同时提出一种基于区间Orthopair模糊距离测度的VIKOR方法,并将其运用到多属性决策方法,通过实例分析,验证所提出方法的可行性和有效性.

属性权重未知情形下的q阶orthopair犹豫模糊TOPSIS法

在多属性决策问题中,q阶orthopair犹豫模糊集可以有效地处理不确定信息。基于Theil不等系数,提出一种新的q阶orthopair犹豫模糊距离测度,并研究该距离测度的性质。针对属性权重完全未知的q阶orthopair犹豫模糊多属性决策问题,构建离差最大化的属性权重模型。基于该权重确定模型和q阶orthopair犹豫模糊距离测度,提出一种改进的q阶orthopair犹豫模糊TOPSIS法。通过能源发展案例说明该方法的可行性和有效性。

一种基于orthopair模糊相似测度的多属性群决策方法

本文针对orthopair模糊信息下的决策问题,提出了一种基于orthopair模糊相似测度的多属性决策方法.首先基于Theil不等系数定义了一种orthopair模糊相似测度,研究了它们的性质,并基于该相似测度,构建两个优化模型分别求解群决策信息为orthopair模糊数问题的专家权重和属性权重,同时给出一种基于orthopair模糊相似测度的多属性群决策方法,最后通过投资选择案例来验证该方法的合理性和有效性。

q阶区间orthopair模糊power平均算子及其应用

将power平均算子推广至q阶区间orthopair模糊环境,提出一种q阶区间orthopair模糊power平均(q-RIVOFPA)算子并研究其性质。同时,给出一种基于q-RIVOFPA算子的模糊多属性决策方法,并通过实例验证其可行性和有效性。