## 4 Full-Ordering Decision Making on Electricity Sales Packages Based on Customer Satisfaction  
The decision-making process of electricity sales companies regarding their packages must prioritize customer satisfaction, as it significantly impacts the likelihood of successful outcomes. Consequently, this paper proposes a comprehensive decision-making framework for electricity sales packages, focused on maximizing customer satisfaction. Firstly, the method involves identifying similar customers to the target customers from historical customers. Secondly, satisfaction levels regarding electricity sales package are evaluated. Finally, by integrating the similarities between target customers and similar customers, the satisfaction level towards electricity sales packages for target customers can be accurately assessed. Based on the customer clustering results obtained in Section 2.2, similar historical customers to the target customer can be identified. The target customer is set to \(U^{\prime}=\{U^{\prime}1,\)\(U^{\prime}2,\)\(\cdots,U^{\prime}i,\)\(\cdots U^{\prime}l\}\), where \(U^{\prime}_{i}\) is the \(i\)-th target customer, and \(I\) is the total number of tar get customers. Based on the portrait \(\overrightarrow{G}_{U^{\prime}_{i}}=\{g_{U^{\prime}_{i},l},g_{U^{\prime}_{i},m}, \delta U_{i^{\prime},j},h,\delta U_{i^{\prime},p},\delta U_{i^{\prime},p},m\}, \delta U_{i^{\prime}_{i},l},\delta U_{i^{\prime},v,m},\)\(g_{U^{\prime}_{i},sl^{\prime},\delta U^{\prime}_{i^{\prime},s},m\}\) of the target customer \(U^{\prime}_{i}\), the distance between the cluster center of the \(M\)-class customer and the \(\overrightarrow{G}_{U^{\prime}_{i}}\) is calculated respectively. The customer in the corresponding class with the smallest distance is determined to be a similar customer to the target customer \(U^{\prime}_{i}\), which is recorded as \(UT_{m}=\{UT_{m,1},UT_{m,2},\cdots,UT_{m,n}\cdots,UT_{m,N}\}\), where \(N\) is the total number of similar customers to target customer \(U^{\prime}_{i}\). \(UT_{m,n}\) is the \(n\)-th customer similar to the target customer \(U^{\prime}_{i}\). Then, the similarity matrix between target customer \(U^{\prime}_{i}\) and similar customers is expressed as \(S=\left(s^{\prime}_{\rho}(U^{\prime}_{i},UT_{m,n})\right)_{I\times N}\). It should be noted that the monthly load data of target customers are unknown and must be predicted using the load forecasting method. Based on the evaluation information of similar customers' electricity sales packages obtained in Section 3, the evaluation information of customers is expected. And the satisfaction matrix \(W_{UT,X}=\left(w_{UT_{m,n},X_{j}}\right)_{N\times J}\) of similar customers' electricity sales packages is obtained. Where \(w_{UT_{m,n},X_{j}}\) is the \(n\)-th customer of similar customers' satisfaction with the electricity sales package \(X_{j}\), we obtain  
\[w_{UT_{m,n},X_{j}}=CL_{UT_{m,n}}\times\sum\limits_{k=1}^{K}\left(A^{UT_{m,n}}_{ j,k}\times\omega_{UT_{m,k}}\right). \tag{13}\]  
The satisfaction of \(m\)-th-type customer \(UT_{m}\) with electricity sales package \(X_{j}\) is  
\[w_{UT_{m},X_{j}}=\frac{1}{N}\sum\limits_{n=1}^{N}\left[CL_{UT_{m,n}}\times \sum\limits_{k=1}^{K}\left(A^{UT_{m,n}}_{j,k}\times\omega_{UT_{m,k}}\right)\right]. \tag{14}\]  
The satisfaction matrix of electricity sales packages for each type of customer is  
\[V_{UT,X}=\left(w_{UT_{m},X_{j}}\right)_{M\times J}. \tag{15}\]  
The satisfaction of the target customer's electricity package depends on the satisfaction of the similar customer's package and the similarity between the target customer and the similar customer.