基于云重心的Topsis网络知识收益分配方法

Network Knowledge Profit Allocation Topsis Model with Membership Cloud Gravity Center

针对网络知识价值链特点和收益分配任务,提出基于云重心的Topsis分配方法。引入正向云发生器,将分配方案中可能的模糊语言型分配值定量化,与数值型分配值构成决策矩阵,解决模糊分配值无法定量考虑的问题。基于Topsis法,构造云重心加权偏离度的拉格朗日函数,得出各成员权重。运用偏离度衡量各方案与理想方案的偏差,根据偏差得出各方案的相对权重,形成综合分配方案,解决多种分配方案协商不一致问题。算例验证了模型的可行性。

For the feature of network knowledge value chain and profit allocation, the paper presents a Topsis allocation method with membership cloud gravity center. To solve the problem that fuzzy - based allocation program can not be con- sidered quantitatively, it introduces forward cloud generator to quantify possible fuzzy linguistic values existing in alloca- tion programs to normalize with those numeric values and to form the decision matrix. Based on Topsis law, by construing the Lagrangian function of weighted cloud gravity center deviation, the paper caculates the weights of various collaborators in the value chain. It also uses the weighted cloud gravity center deviation to measure the bias between the program and the ideal one. And it at last obtains the relative weight of each program according to the size of its deviation. Thus, it constructs the integrated distribution as to address inconsistency issue in various distribution options. It provides a numer- ical example of the model.