基于直觉语言粗糙CoCoSo-H的废旧动力电池回收技术选择模型

Spent Power Battery Recycling Technology Selection Based on Integrated CoCoSo and Heronian Mean with Intuitionistic Linguistic Rough Number

如何选择最优的废旧动力电池回收技术(SPBRT)是废旧动力电池回收利用管理中一个重要的群决策问题。首先,本文在直觉语言粗糙数(ILRN)基础上定义了直觉语言粗糙Heronian平均(ILRHM)和直觉语言粗糙几何Heronian平均(ILRGHM)算子及其加权形式,并讨论相关性质和特例。然后,提出多属性直觉语言粗糙群决策模型,包括:利用决策者评价信息和偏离度最大化原则确定属性的组合权重,通过直觉语言粗糙加权Heronian平均(ILRWHM)和直觉语言粗糙加权几何Heronian平均(ILRWGHM)算子改进传统CoCoSo方法,以及考虑决策者风险偏好评价备选方案并做出最优决策。该方法能捕获属性之间的关联关系,能实现决策者乐观和悲观决策态度的均衡,引入决策者风险偏好系数使方案排序更具灵活性,通过决策参数变化分析能检测出参数对决策结果的影响具有鲁棒性。最后,为了验证本文方法的有效性和合理性,以SPBRT选择为例进行分析,通过参数分析和与其他方法对比分析说明本文方法的优越性。

How to select the optimal spent power battery recycling technology(SPBRT)is an important group decision-making problem in the recycling management of waste power battery.Firstly,the intuitionistic linguistic rough Heronian mean(ILRHM)and intuitionistic linguistic rough geometric Heronian mean(ILRGHM)operators and their weighted forms are defined based on the intuitionistic linguistic rough numbers(ILRNs)in this paper,and the related some properties and special cases are discussed.Then,a multi-attribute intuitionistic linguistic rough group decision-making model is proposed,which includes the combination weights of attributes are determined by the decision maker’s evaluation information and the deviation maximization principle,the traditional CoCoSo method is improved by using intuitionistic linguistic rough weighted Heronian mean(ILRWHM)and intuitionistic linguistic rough weighted geometric Heronian mean(ILRWGHM),and evaluate alternatives and make the optimal decision considering the decision maker’s risk preference.This method(1)can capture the correlation between attributes,(2)can realize the balance between optimistic and pessimistic decision-making attitudes of decision makers,(3)introduces the risk preference coefficient of deci⁃sion makers to make alternative ordering more flexible,and(4)can detect the influence of parameters on deci⁃sion results through the change analysis of decision-making parameters and has robustness.Finally,in order to verify the effectiveness and rationality of the proposed method,SPBRT selection is taken as an example to demonstrate the advantages of this method through effect of parameters and comparative analysis with other methods.