Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockch...Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant.We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants.To determine the barriers,we conducted a literature review.We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy(IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS(ranking sum)methods to determine the barrier weights.To evaluate the barrier level of each alternative participant in SCF,the integrated IVFF-RAFSI(Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval)model is presented to rank the barrier,which uses a power-weighted aggregation operator to fuse experts’opinions.A case study demonstrates the practicality of the integrated IVFF-RAFSI model.The results show that uncertain and competitive markets(weighted at 0.0676)are the most significant barriers.This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption.Sensitivity and comparative analyses validate the steadiness and competency of the proposed model.These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.展开更多
In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle...In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle uncertain information,Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making(MADM)problems.This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership,non-membership,and priority are considered simultaneously.Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators,this paper proposes the Fermatean hesitant fuzzy Heronian mean(FHFHM)operator and the Fermatean hesitant fuzzyweighted Heronian mean(FHFWHM)operator.Then,considering the priority relationship between attributes is often easier to obtain than the weight of attributes,this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator(FHFPHM),and discusses its elegant properties such as idempotency,boundedness and monotonicity in detail.Later,for problems with unknown weights and the Fermatean hesitant fuzzy information,aMADM approach based on prioritized attributes is proposed,which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results.Finally,a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.展开更多
Uncertainty and ambiguity are pervasive in real-world intelligent systems,necessitating advanced mathematical frameworks for effective modeling and analysis.Fermatean fuzzy sets(FFSs),as a recent extension of classica...Uncertainty and ambiguity are pervasive in real-world intelligent systems,necessitating advanced mathematical frameworks for effective modeling and analysis.Fermatean fuzzy sets(FFSs),as a recent extension of classical fuzzy theory,provide enhanced flexibility for representing complex uncertainty.In this paper,we propose a unified parametric divergence operator for FFSs,which comprehensively captures the interplay among membership,nonmembership,and hesitation degrees.The proposed operator is rigorously analyzed with respect to key mathematical properties,including non-negativity,non-degeneracy,and symmetry.Notably,several well-known divergence operators,such as Jensen-Shannon divergence,Hellinger distance,andχ2-divergence,are shown to be special cases within our unified framework.Extensive experiments on pattern classification,hierarchical clustering,and multiattribute decision-making tasks demonstrate the competitive performance and stability of the proposed operator.These results confirm both the theoretical significance and practical value of our method for advanced fuzzy information processing in machine learning and intelligent decision-making.展开更多
This paper develops twin models towards integrated production inventory planning for manufacturer–retailer ecosystem in a sustainable supply chain setup.Decision-making models are developed in fuzzy environment and u...This paper develops twin models towards integrated production inventory planning for manufacturer–retailer ecosystem in a sustainable supply chain setup.Decision-making models are developed in fuzzy environment and under purview of carbon taxation system.Novel conception of Fermatean fuzzy numbers is introduced for handling parameters imprecision.The first model addresses planning problem without considering green investments,whereas the second one additionally identifies optimal green investments for each player of ecosystem.Models are formulated as nonlinear optimization problems with objective of maximizing profit.Comparison of results from both models enables decision-makers to figure out the profitability of green investment option.Numerical instance with data from the existing literature is solved using Mathematica 12.1.Computational results for studied case report profitability of green investments for supply chain partners and significant reduction in carbon emissions as well.Variation analysis demonstrates stability of the proposed model.Developed models equip small-scale retailer-manufacture tie-ups prevalent in developing economies for discussed decisions.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.72101004)the Humanity and Social Science Research Project of the Anhui Educational Committee(2023AH030053).
文摘Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant.We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants.To determine the barriers,we conducted a literature review.We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy(IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS(ranking sum)methods to determine the barrier weights.To evaluate the barrier level of each alternative participant in SCF,the integrated IVFF-RAFSI(Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval)model is presented to rank the barrier,which uses a power-weighted aggregation operator to fuse experts’opinions.A case study demonstrates the practicality of the integrated IVFF-RAFSI model.The results show that uncertain and competitive markets(weighted at 0.0676)are the most significant barriers.This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption.Sensitivity and comparative analyses validate the steadiness and competency of the proposed model.These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.
文摘In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle uncertain information,Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making(MADM)problems.This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership,non-membership,and priority are considered simultaneously.Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators,this paper proposes the Fermatean hesitant fuzzy Heronian mean(FHFHM)operator and the Fermatean hesitant fuzzyweighted Heronian mean(FHFWHM)operator.Then,considering the priority relationship between attributes is often easier to obtain than the weight of attributes,this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator(FHFPHM),and discusses its elegant properties such as idempotency,boundedness and monotonicity in detail.Later,for problems with unknown weights and the Fermatean hesitant fuzzy information,aMADM approach based on prioritized attributes is proposed,which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results.Finally,a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.
文摘Uncertainty and ambiguity are pervasive in real-world intelligent systems,necessitating advanced mathematical frameworks for effective modeling and analysis.Fermatean fuzzy sets(FFSs),as a recent extension of classical fuzzy theory,provide enhanced flexibility for representing complex uncertainty.In this paper,we propose a unified parametric divergence operator for FFSs,which comprehensively captures the interplay among membership,nonmembership,and hesitation degrees.The proposed operator is rigorously analyzed with respect to key mathematical properties,including non-negativity,non-degeneracy,and symmetry.Notably,several well-known divergence operators,such as Jensen-Shannon divergence,Hellinger distance,andχ2-divergence,are shown to be special cases within our unified framework.Extensive experiments on pattern classification,hierarchical clustering,and multiattribute decision-making tasks demonstrate the competitive performance and stability of the proposed operator.These results confirm both the theoretical significance and practical value of our method for advanced fuzzy information processing in machine learning and intelligent decision-making.
文摘This paper develops twin models towards integrated production inventory planning for manufacturer–retailer ecosystem in a sustainable supply chain setup.Decision-making models are developed in fuzzy environment and under purview of carbon taxation system.Novel conception of Fermatean fuzzy numbers is introduced for handling parameters imprecision.The first model addresses planning problem without considering green investments,whereas the second one additionally identifies optimal green investments for each player of ecosystem.Models are formulated as nonlinear optimization problems with objective of maximizing profit.Comparison of results from both models enables decision-makers to figure out the profitability of green investment option.Numerical instance with data from the existing literature is solved using Mathematica 12.1.Computational results for studied case report profitability of green investments for supply chain partners and significant reduction in carbon emissions as well.Variation analysis demonstrates stability of the proposed model.Developed models equip small-scale retailer-manufacture tie-ups prevalent in developing economies for discussed decisions.
