This study proposes a multiple attribute group decisionmaking(MAGDM)approach on the basis of the plant growth simulation algorithm(PGSA)and interval 2-tuple weighted average operators for uncertain linguistic weighted...This study proposes a multiple attribute group decisionmaking(MAGDM)approach on the basis of the plant growth simulation algorithm(PGSA)and interval 2-tuple weighted average operators for uncertain linguistic weighted aggregation(ULWA).We provide an example for illustration and verification and compare several aggregation operators to indicate the optimality of the assembly method.In addition,we present two comparisons to demonstrate the practicality and effectiveness of the proposed method.The method can be used not only to aggregate MAGDM problems but also to solve multi-granularity uncertain linguistic information.Its high reliability,easy programming,and high-speed calculation can improve the efficiency of ULWA characteristics.Finally,the proposed method has the exact characteristics for linguistic information processing and can effectively avoid information distortion and loss.展开更多
Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose ch...Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.展开更多
基金supported by the National Natural Science Foundation of China(71771118 71471083)+1 种基金the Ministry of Education Humanities and Social Sciences Foundation of China(18YJCZH146)the Nanjing University Double First-Class project
文摘This study proposes a multiple attribute group decisionmaking(MAGDM)approach on the basis of the plant growth simulation algorithm(PGSA)and interval 2-tuple weighted average operators for uncertain linguistic weighted aggregation(ULWA).We provide an example for illustration and verification and compare several aggregation operators to indicate the optimality of the assembly method.In addition,we present two comparisons to demonstrate the practicality and effectiveness of the proposed method.The method can be used not only to aggregate MAGDM problems but also to solve multi-granularity uncertain linguistic information.Its high reliability,easy programming,and high-speed calculation can improve the efficiency of ULWA characteristics.Finally,the proposed method has the exact characteristics for linguistic information processing and can effectively avoid information distortion and loss.
基金supported by the Fundamental Research Funds for the Central Universities(22120240094)Humanities and Social Science Fund of Ministry of Education China(22YJA630082).
文摘Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.
