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作 者:戚筱雯 张俊岭[2] 梁昌勇[3] QI Xiaowen;ZHANG Junling;LIANG Changyong(School of Business Administration,Zhejiang University of Finance&Economics,Hangzhou 310018,China;School of Economics and Management,Zhejiang Normal University,Jinhua 321004,China;School of Management,Hefei University of Technology,Hefei 230009,China)
机构地区:[1]浙江财经大学工商管理学院,杭州310018 [2]浙江师范大学经济与管理学院,金华321004 [3]合肥工业大学管理学院,合肥230009
出 处:《系统工程理论与实践》2022年第8期2243-2257,共15页Systems Engineering-Theory & Practice
基 金:国家社会科学基金(17BJY159)。
摘 要:双边概率语言术语集能在高度不确定性决策环境下更细致完整地刻画决策者复杂评价信息,其决策理论与方法亟需深入研究.本文针对具有双层评价指标结构且权重未知的典型复杂决策问题,提出一种更具实际适应性的双边概率语言科学决策方法.首先,提出基于最小公倍数的犹豫模糊元拓展法以避免主观态度假设等限制及相应潜在信息扭曲,并藉此定义了双边概率语言距离测度、熵和相对熵测度.其次,利用熵和交叉熵开发二级指标的客观权重确定模型,并引入BWM方法来确定一级指标的主观权重.进一步,基于上述方法与模型构建了面向双层评价指标结构决策问题的双边概率语言多准则决策方法.最后,可持续供应商选择案例研究与实验对比分析验证了所研决策方法的有效性与优越性.Tackling hesitant fuzzy decision making with complicate evaluations in form of dual probabilistic linguistic term set(DPLTS),this paper introduces an extension method based on least common multiple(LCM)for hesitant fuzzy element as well as its derived fuzzy entropy measures for DPLTS and puts forward an effective approach for a typical type of problems with bi-layered index system and unknown criteria weights.Firstly,theoretical analysis shows that the introduced LCM-based extension method avoids potential information distortion brought by traditional methods.On the ground of the LCM-based extension method,some fuzzy measures including distance measure,entropy measure and cross-entropy measure have been developed for DPLTS.Secondly,to deduce appropriate criteria weights,a hybrid model is developed to generate weighting vector for the second-layer criteria by utilizing the proposed entropy and cross-entropy measures,and another BWM-based model is also utilized to compute weighting vector for the first-layer criteria.Further,based on the afore-proposed models,our proposed decision making approach is then constructed.Finally,case studies on sustainable supplier selection and experimental studies have been conducted to verify our approach.
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