## Materials and Methods
### Operational Efficiency Evaluation Model Based on Dynamic Data Entvelopment Analysis (D-DEA)  
Data Envelopment Analysis, which is based on the concept of relative efficiency, can be used in efficiency evaluation as a non-parametric statistical method to evaluate the efficiency of decision-making units with the same type of multiple input and output [11; 12; 13]. The idea is to regard an economic system as a unit (called a Decision Making Unit (DMU)), and make a \"product\" by putting in production factors on the grandest scale possible. The evaluation group is composed of DMUs where through the analysis of input or output ratio, evaluation variables are index weights of the DMU. Next, the efficient production frontier is determined and the validity of each DMU can be known by the distance between the DMU and the frontier. At the same time, the projection method can be used to point out the improved direction.  
For model selection, this study focused on two aspects. First, compared to other models, DEA models are not only used to obtain the efficiency of each DMU, but also to solve problems where the data contain negative values due to their translation invariance. Second, [PERSON] and [PERSON] [14] classified the constraints of linking two types: the free link and the fixed link. With respect to the free link case, the linking activities were freely determined whilst continuity between the input and output was maintained. This case demonstrated whether the current link flow was appropriate in light of the other DMUs. With respect to the fixed link case, the linking activities were kept unchanged.  
This model also considered the time continuity of enterprise development, which means that the grid construction effect of the last time-period is the initial condition of the next time. The management efficiency evaluation model is dynamic, and so defined as the Dynamic Data Envelopment Analysis. At the same time, it considers the relevance of different attributes, that is, the benefit of construction  
Figure 1: Basic workflow of the Interpretive Structural Model (ISM).  
investment from a property may become the investment of another property. Thus, the target function of the D-DEA was as follows:  
\[\min\frac{\sum\limits_{t=1}^{T}W^{t}\bigg{(}\sum\limits_{k=1}^{K}\omega^{k} \bigg{(}1-\frac{1}{m_{k}}\sum\limits_{i=1}^{m_{k}}\frac{s_{ijk}^{t-}}{x_{ik}^{ t}}\bigg{)}\bigg{)}}{\sum\limits_{t=1}^{T}W^{t}\bigg{(}\sum\limits_{k=1}^{K} \omega^{k}\bigg{(}1+\frac{1}{r_{k}}\sum\limits_{i=1}^{r_{k}}\frac{s_{nk}^{t+} }{y_{nk}^{t}}\bigg{)}\bigg{)}} \tag{1}\]  
where \(x_{ijk}^{t}\) is the value of the input index \(i\) in \(t\) year; \(y_{rjk}^{t}\) is the value of output index \(r\) in \(t\) year; \(s_{ijk}^{t-}\) is the slack variable of input index \(i\) in \(t\) year; and \(s_{rjk}^{t+}\) is the slack variable of output index \(r\) in \(t\) year.  
The constraint conditions were as follows:  
\[st.\left\{\begin{array}{l}\sum\limits_{j=1}^{n}\lambda_{jk}^{t}x_{ijk}^{t}+s _{ik}^{t-}=x_{iok}^{t},o=1,\ldots,n;k=1,\ldots,K;i=1,\ldots,m;t=1,\ldots,T\\ \sum\limits_{j=1}^{n}\lambda_{jk}^{t}y_{rjk}^{t}-s_{rk}^{t-}=y_{rok}^{t},o=1, \ldots,n;k=1,\ldots,K;r=1,\ldots,s;t=1,\ldots,T\\ \sum\limits_{j=1}^{n}\lambda_{jk}^{t}z_{jk_{i}}^{t+1}=\sum\limits_{j=1}^{n} \lambda_{jk}^{t+1}z_{jk_{i}}^{t,t+1}\\ \