当前位置: X-MOL 学术Comput. Ind. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Dual hesitant fuzzy decision making in optimization models
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2021-01-16 , DOI: 10.1016/j.cie.2021.107103
Fanyong Meng , Jie Tang , Witold Pedrycz

Considering the advantages of dual hesitant fuzzy elements (DHFEs) for describing the hesitant and intuitionistic judgments of experts and identifying the limitations of previous research about dual hesitant fuzzy decision making, this paper studies decision making with dual hesitant fuzzy preference relations (DHFPRs) and provides a new group decision making (GDM) method based on a series of built optimization models. A multiplicative consistency concept for DHFPRs is first introduced and analyzed. On the basis of this concept, optimization describing the multiplicative consistency of DHFPRs is realized. Meanwhile, stepwise optimization models for obtaining complete multiplicatively consistent DHFPRs are constructed, and a method for calculating the intuitionistic fuzzy priority vector is offered. Then, optimization models for determining unknown values in incomplete DHFPRs are formed in view of the multiplicative consistency. An index for measuring the consensus is defined and optimization models for reaching the consensus requirement of a group are established. Furthermore, consensus-based optimization models for determining the weights of experts are established. In view of the multiplicative consistency and consensus analysis, a detailed procedure for GDM with incomplete and inconsistent DHFPRs is provided. Finally, the new method is illustrated by a case study concerning an evaluation of the environmental performance of enterprises and associated comparative analyses are reported.



中文翻译:

优化模型中的双重犹豫模糊决策

考虑到双重犹豫模糊元素(DHFE)在描述专家犹豫和直觉判断上的优势以及确定先前关于双重犹豫模糊决策的研究的局限性,本文研究了具有双重犹豫模糊偏好关系(DHFPRs)的决策一种基于一系列已建立的优化模型的新的群体决策(GDM)方法。首先介绍并分析了DHFPR的乘法一致性概念。基于此概念,实现了描述DHFPR的乘法一致性的优化。同时,建立了获得完整的乘性一致的DHFPR的逐步优化模型,并提供了一种计算直觉模糊优先级向量的方法。然后,鉴于乘法一致性,形成了用于确定不完整DHFPR中未知值的优化模型。定义了衡量共识的指标,建立了达到群体共识要求的优化模型。此外,建立了用于确定专家权重的基于共识的优化模型。鉴于乘法一致性和共识分析,提供了具有不完整和不一致的DHFPR的GDM的详细过程。最后,通过对企业环境绩效评价的案例研究说明了该新方法,并报告了相关的比较分析。定义了衡量共识的指标,建立了达到群体共识要求的优化模型。此外,建立了用于确定专家权重的基于共识的优化模型。鉴于乘法一致性和共识分析,提供了具有不完整和不一致的DHFPR的GDM的详细过程。最后,通过对企业环境绩效评价的案例研究说明了该新方法,并报告了相关的比较分析。定义了衡量共识的指标,建立了达到群体共识要求的优化模型。此外,建立了用于确定专家权重的基于共识的优化模型。鉴于乘法一致性和共识分析,提供了具有不完整和不一致的DHFPR的GDM的详细过程。最后,通过对企业环境绩效评价的案例研究说明了该新方法,并报告了相关的比较分析。提供了具有不完整和不一致的DHFPR的GDM的详细过程。最后,通过对企业环境绩效评价的案例研究说明了该新方法,并报告了相关的比较分析。提供了具有不完整和不一致的DHFPR的GDM的详细过程。最后,通过对企业环境绩效评价的案例研究说明了该新方法,并报告了相关的比较分析。

更新日期:2021-02-07
down
wechat
bug