基于犹豫模糊语言术语的供应商多准则群决策研究

摘要

In order to make scientific decisions of suppliers under the global value chain environment, a multi-criteria group decision-making model based on uncertain linguistic terms was proposed. Preference information, first elicited from experts, was transformed into hesitant fuzzy linguistic terms and computed with words via uncertain linguistic variables. The expert group's preference information was then fused by the envelope operator and hesitant fuzzy linguistic term set built. Then the relative closeness coefficient was adopted to sort the production suppliers. Consequently, the most satisfactory supplier was selected. In addition, information entropy was proposed for solving the weights without prior knowledge of multi-criteria decision-making process. The results show that the most satisfactory selection results are the same under 3 different information entropy parameters. Moreover, the ranking results of the relative closeness coefficient for supplier selections are not sensitive to the change of information entropy parameters, which verifies the feasibility, effectiveness and stability of the proposed model. Therefore, the proposed model can provide a useful reference for the practical application of auto parts suppliers' evaluation and selection.%针对全球价值链环境下供应商科学决策问题,提出基于不确定语言术语的多准则群决策模型.首先分别提取专家的偏好信息,将偏好信息转化为犹豫模糊语言术语,引入不确定语言变量进行词计算;其次,运用包络算子融合专家的偏好信息形成犹豫模糊语言术语集,设计集成准则权重的相对贴近度进行产品供应商排序,确定最满意供应商;此外,引入信息熵求解决策过程无先验知识的多准则权重;计算结果表明:3种信息熵参数条件下最满意汽车零部件供应商选择结果完全一致,基于相对贴近度值的供应商优劣排序结果相对于信息熵参数变化不敏感;验证了所提模型可行性、有效性和稳定性,为汽车零部件供应商的实际评价与选择提供有益借鉴.