Fuzzy multiple attribute decision-making model for synergic knowledge innovation in supply chain

The procedure of supply chain development is the process of continuously congregating knowledge and transforming knowledge.First,the precondition of synergic knowledge innovation in the supply chain is narrated.Then the characteristics of synergic knowledge innovation in the supply chain are analyzed,including complexity,accumulating and evolving process,and the cooperation of members and network integration.Due to the characteristics of multi-factors and uncertainties of the supply chain system,the fuzzy multi-attribution group decision-making model is introduced to solve the involved problem of synergic knowledge innovation in the supply chain.After elaborating on steps of using the fuzzy multiple attribute decision-making(MADM)model,the procedure of decision making for synergic knowledge innovation in the supply chain is explained from an example in the application of a fuzzy MADM model.The fuzzy MADM model,which amalgamates intuition and resolution decision-making can effectively improve the rationality of decision-making for synergic knowledge innovation in the supply chain.

Cotangent Similarity Measure of Consistent Neutrosophic Sets and Application to Multiple Attribute Decision-Making Problems in Neutrosophic Multi-Valued Setting

A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and consistency degree of true,false,and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS.However,there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature.This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting.The cosine similarity measures showthe cosine of the angle between two vectors projected into amultidimensional space,rather than their distance.The cotangent similaritymeasures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent.Then,a decisionmaking approach is presented in viewof the established cotangent similarity measures in the case of NMVSs.Finally,the developed decision-making approach is applied to selection problems of potential cars.The proposed approach has obtained two different results,which have the same sort sequence as the compared literature.The decision results prove its validity and effectiveness.Meantime,it also provides a new manner for neutrosophic multi-valued decision-making issues.

Solution to multiple attribute group decision making problems with two decision makers

A kind of multiple attribute group decision making （MAGDM） problem is discussed from the perspective of statistic decision-making. Firstly, on the basis of the stability theory, a new idea is proposed to solve this kind of problem. Secondly, a con- crete method corresponding to this kind of problem is proposed. The main tool of our research is the technique o~ the jackknife method. The main advantage of the new method is that it can identify and determine the reliability degree of the existed decision making information. Finally, a traffic engineering example is given to show the effectiveness of the new method.

Performance Evaluation of Enterprise Knowledge Management based on Multiple Attribute Group Decision Making

Given that the classical performance evaluation models can not deal with the group decision making problems since they simply average the index,we propose an enterprise knowledge management evaluation model based on multiple attribute group decision making (MAGDM). Find the differences between Ordered Weighted Averaging (OWA) and methods for uncertain decision making. Also,analyze the multiple attribute group decision making process and implement the algorithm. Finally,apply the method on performance evaluation of four enterprises and make sensitivity analysis towards the evaluation results.

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative consistent for IFPRs is defined,and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs.Moreover,in this study,another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs.For group decisionmaking(GDM)with incomplete IFPRs,three reliable sources influencing the weights of experts are identified.Subsequently,a method for determining the weights of experts is developed by simultaneously considering three reliable sources.Furthermore,a targeted consensus process(CPR)is developed in this study with reference to the actual situation of the consensus level of each IFPR.Meanwhile,in response to the proposed multiplicative consistency definition,a novel method for determining the optimal priority weights of alternatives is redefined.Lastly,based on the above theory,a novel GDM method with incomplete IFPRs is developed,and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.

Aczel-Alsina Weighted Aggregation Operators of Simplified Neutrosophic Numbers and Its Application in Multiple Attribute Decision Making

The simplified neutrosophic number(SNN)can represent uncertain,imprecise,incomplete,and inconsistent information that exists in scientific,technological,and engineering fields.Hence,it is a useful tool for describing truth,falsity,and indeterminacy information in multiple attribute decision-making(MADM)problems.To suit decision makers’preference selection,the operational flexibility of aggregation operators shows its importance in dealing with the flexible decision-making problems in the SNN environment.To solve this problem,this paper develops the Aczel-Alsina aggregation operators of SNNs for MADM problems in view of the Aczel-Alsina operational flexibility.First,we define the Aczel-Alsina operations of SNNs.Then,the Aczel-Alsina aggregation operators of SNNs are presented based on the defined Aczel-Alsina operations of SNNs.Next,a MADM method is established using the proposed aggregation operators under the SNN environment.Lastly,an illustrative example about slope treatment scheme choices is provided to indicate the practicality and efficiency of the established method.By comparison with the existing relative MADM methods,the results show that the established MADM method can overcome the insufficiency of decision flexibility in the existing MADM methods and demonstrate the metric of flexible decision-making.

Group training on the improvement of college students'career decision-making self-efficacy

A group training was conducted on 17 college students to improve their career decision-making self-efficacy (CDMSE). The result showed that there was significant difference between the pre-test and the post-test for the experimental group (n = 17), whereas no significant difference was found between the pre-test and the post- test for the control group (n = 17). In the pre-test, there was no significant difference between the experimental group and the control group, and obvious difference between the two groups was found in the post-test. This indicated that the group training was effective on improving the CDMSE of the college students whose scores of CDMSE were below 27% point of the total students.

Extension of Paraconsistent Many-Valued Similarity Method to Group Decision

The Paraconsistent Many-Valued Similarity (PMVS) method for multi-attribute decision making will be incomplete as a decision model if it is not extended to the realm of group decision-making. Therefore, in this article, our primary objective is to show how the paraconsistent many-valued similarity method can be used to solve group decision-making problems involving choice making or ranking of a finite set of decision alternatives. Moreover, since weights are very important parameters in multi-attribute decision-making, we have introduced the Borda rule to calculate the weights of experts and that of every criterion under consideration. To demonstrate how the proposed method works, a numerical example on energy sources of an economy from the points of view of a group of experts is investigated. Further, we compare the results of this new approach with that of fuzzy TOPSIS group decision-making method to illustrate the robustness and effectiveness of the former.

球型模糊环境下基于前景理论和VIKOR的多属性群决策方法研究

研究了模糊多属性群决策问题。首先,在属性权重已知的情况下,通过建立评价属性,专家打分构建球型模糊评价矩阵。其次,考虑专家的有限理性行为,基于前景理论构建了前景值决策矩阵,并运用VIKOR方法,在可接受优势和决策过程稳定的条件下对方案进行择优。最后,通过算例与3种方法对比,说明了方法的有效性和可行性。

基于直觉模糊语言集的直播带货窗口选择方法

商家利用职业直播带货主播推销产品已经成为当下最为火热的销售形式,本文利用基于直觉模糊语言集的群体多属性决策方法,为商家选择最优直播带货窗口提供了理论指导。在群体多属性决策方法的制定中,本文利用将语言变量转化为直觉模糊数的方式,对语言文本进行转化处理。分别考虑决策者与专家风险偏好的拟合程度,提出了一种将专家主观权重和客观权重相结合的专家权重确定方式。在对权重与直觉模糊集进行集合时,提出了一种全新的直觉模糊幂混合平均算子(IFPHA),并优化了集合过程中排序使用的得分函数。最后,采用topsis法,并结合改进的直觉模糊数相似度计算公式对最终方案进行了比较,选出最适合风险偏好中等商家的带货直播窗口。

阿基米德copula概率对偶犹豫模糊信息集成算子及其在多属性群决策中的应用

本文基于阿基米德copula和相应的协copula定义了概率对偶犹豫模糊信息的一些运算法则,为概率对偶犹豫模糊信息的融合提供了一种新思路。这些运算法则不仅可以保持运算的封闭性,而且可以反映出概率对偶犹豫模糊信息之间的关系。在进一步研究运算法则性质的基础上,提出了阿基米德copula加权概率对偶犹豫模糊算术平均算子和几何平均算子,并讨论了其性质。针对概率对偶犹豫模糊信息环境下属性权重信息未知的多属性群决策问题,给出了求解属性权重的公式,并利用上述算子进行信息集结,获得了群决策方法。最后,通过公众生态环境满意度评价的实例说明所提方法的有效性,利用参数对评价结果的影响说明决策方法的灵活性。此外,还将本文方法与现有方法进行了对比分析。

基于模糊多属性群决策的变压器状态维修策略研究

提出了一种电力变压器状态维修策略的模糊多属性群决策方法,并通过某一变压器状态维修方案的决策过程,采用折衷型群决策方法具体探讨了模糊多属性群决策方法在变压器状态维修决策中的应用,其结果表明本方法是可行的。

多层次交互式确定权重方法的改进

提出了一种改进的多层次交互式决策模型的权重确定方法.该模型首先由参加决策的上级邀请专家确定权重约束范围和初始参考权重向量,再由各下级在专家约束下,根据自己的偏好确定各自的权重,然后提交上级,并由上级进行综合,确定系统的参考权重;然后反馈给各单元,由各单元再次确定各自的权重,并提交上级进行综合,这样不断反馈,直至满足一定要求后终止,由上级最终确定系统综合权重.最后,以一个算例来说明本文方法的实施过程.

对偶犹豫模糊集的相关系数及其应用

对偶犹豫模糊集因其可以给决策者提供更多的决策信息成为模糊决策的热点研究问题,相关性指标可以用来度量两个模糊信息之间的相关关系,熵可以用来度量模糊信息的不确定程度。提出了一种基于对偶犹豫模糊集相关系数和熵的模糊多属性群决策方法。定义了对偶犹豫模糊集相关系数的概念,讨论了其基本性质;提出了两种对偶犹豫模糊集的熵,在此基础上,给出了模糊多属性群决策的权重确定方法;基于对偶犹豫模糊集相关系数和熵,提出了一种属性权重完全未知条件下的模糊多属性群决策方法;通过案例分析说明了该方法的有效性和可行性。

多层次交互式确定权重的方法

提出了一种多层次交互式决策模型的权重确定方法 .该模型由上级确定各指标权重的范围和某些规则 ,各下级在上级约束下 ,根据自己的偏好确定各自的权重后提交上级 ,并由上级进行综合 ,确定系统的参考权重 ;然后反馈给各单元 ,由各单元再次确定各自的权重 ,并提交上级进行综合 ,这样不断反馈 ,直至满足一定要求后终止 ,由上级最终确定系统综合权重 .最后 。

一种直觉模糊多属性群决策方法及其在群决策中的应用

对以直觉模糊数形式表示的信息和属性权重完全未知的多属性群决策问题进行了研究。提出了一种基于熵值的直觉模糊数距离测度方法,同时对传统的比较得分函数和精确函数的直觉模糊数排序方法进行了改进,定义了一种新的排序公式;进而利用此距离度量公式,引入到基于直觉模糊数之间距离的离差最大化方法中,确定属性的权重,提出了一种基于属性权重完全未知的直觉模糊多属性群决策方法。最后,将此方法运用在ERP选型中。

基于直觉模糊动态信息交互的多属性群决策模型

针对目前的多属性群决策研究中很少考虑信息交互环节等问题,提出一种基于直觉模糊动态信息交互的多属性群决策模型。模型中用直觉模糊集表示决策属性值,通过定义两直觉模糊集的相似度,计算各个专家对方案的偏好矢量与理想方案偏好矢量间的相似度。通过计算群体的平均相似度和专家的决策偏差,若决策偏差大于给定阈值,则通过信息交互对决策偏好进行修正,直到所有的专家的决策偏差都小于给定阈值为止。计算各个方案与理想方案的加权相似度,按加权相似度排列,根据加权相似度最大原则选出最优方案。最后,通过结合1个实际案例讨论本模型的实施步骤,并与未考虑动态信息交互的模糊动态信息群决策方法进行比较,结果证明它是可行和有效的。

区间直觉模糊多属性群决策自收敛算法

对区间直觉模糊多属性群决策中的一致性问题进行研究,引入了一种可自收敛算法得到一致性群决策矩阵。首先基于区间直觉模糊数的运算法则和集结算子,将单个决策者的决策矩阵集结为群体决策矩阵;然后通过可自收敛算法反复迭代,直到得到满足一致性的群体决策矩阵;最后再次基于区间直觉模糊数的集结算子和排序规则实现方案选优。从理论上论证了该算法的收敛性和保序性,并通过算例验证了算法的可行性和有效性。

不完全信息下的群体多属性决策方法

针对群体多属性决策问题,提出一种新的不完全信息下的群体多属性决策方法。该方法假设方案在主观评价属性下的评价值采用区间数的形式来表示专家评价值的不确定性和模糊性。针对每一主观评价属性设定一组与之对应的专家重要性权重值,反映决策者在不同评价属性中的重要性程度,同时考虑到专家评价值的相似度,将专家评价值进行集结得到专家群体关于方案集的决策矩阵。计算各方案与理想方案的相似度,并以此对方案进行优劣排序。通过实例说明所提方法的可行性。