## 2 Materials and Methods
### Evaluation Model Construction and Sensitivity Analysis
#### 2.3.1 FCE Model of Supply Chain Services  
The specific process of the FCE of the research subject is as follows:  
1. Determine the set of evaluation factors and their set of rubrics. 1. Determination of evaluation factor set: 1. First-tier Index Set: 1. [U1, U2, U3, U4] = [coordination of capital flow, coordination of logistics, coordination of business flow, coordination of information flow]. Secondary Index Set: 1. [U11, U12, U13, U14] = [convenience of payment, flexible payment capability, timeliness of refunds, average price advantage in the same category]. 2. [U21, U22, U23, U24] = [fresh products delivery capability, product integrity, the level of packaging for freshness preservation, ability for circulation processing]. 3. [U31, U32, U33, U34, U35] = [length of consumption time, flexibility level in terms of delivery, flexibility level of varieties, level of flexibility in terms of the quantity of products, brand image of the supply chain]. 4. [U41, U42, U43, U44] = [frequency of information exchange, speed of collaborative response, information processing reliability, level of information system]. 2. Determine the set of rubrics. In this paper, the Likert satisfaction scale was used for measurements, and the set of rubrics in the evaluation of customers' satisfaction with the quality of the fresh agricultural product services can be expressed as V = [V1, V2, V3, V4, V5]. These correspond with five levels, ranging from unsatisfactory to satisfactory, with scores corresponding to [20, 40, 60, 80, 100]. 2. Determine the weight vector W for each index set. Combined with the weights of each evaluation index, as determined by the previous hierarchical analysis, the additional weight sets of the service quality indexes at all levels of the fresh agricultural produce supply chain platform were obtained. They are as follows: Weight sets of the service quality evaluation indexes at the first level: 1. [W = (W1, W2, W3, W4).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Matrix Order** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline RI & 0.0000 & 0.0000 & 0.5189 & 0.8638 & 1.0959 & 1.2550 & 1.3390 & 1.3954 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the average random consistency index (RI).  
Weight sets of the service quality evaluation indexes at the second level:  
\(\text{W}_{1}=(\text{W}_{11},\text{W}_{12},\text{W}_{13},\text{W}_{14})\).  
\(\text{W}_{2}=(\text{W}_{21},\text{W}_{22},\text{W}_{23},\text{W}_{24})\).  
\(\text{W}_{3}=(\text{W}_{31},\text{W}_{32},\text{W}_{33},\text{W}_{34},\text{W}_{35})\).  
\(\text{W}_{4}=(\text{W}_{41},\text{W}_{42},\text{W}_{43},\text{W}_{44})\).  
(3) Construct the fuzzy evaluation matrix R.  
\[R=\begin{bmatrix}r_{11}&r_{12}&\dots&r_{1n}\\ r_{21}&r_{22}&\dots&r_{2n}\\ \dots&\dots&\dots&\dots\\ r_{m1}&r_{m2}&\dots&r_{mn}\end{bmatrix} \tag{7}\]  
The fuzzy evaluation matrix can fuzzily reflect the relationship between service quality evaluation factors and evaluation scales in the fresh agricultural produce supply chain platform, which means that this matrix is a fuzzy mapping relationship.  
\(r_{ij}\) denotes the affiliation of a certain index \(U_{i}\) to the evaluation level \(V_{j}\).  
(4) Based on the collected evaluation data, the judging results of each program were determined.  
By collecting statistical questionnaires and determining the fuzzy evaluation matrix R, the FCE model S was finally derived.  
\[S=W\ast R=(S1,S2,\dots,Sn) \tag{8}\]  
(5) Determine the composite score of each evaluation object.  
\[B=S^{\prime}\ast VT \tag{9}\]  
where \(S^{\prime}\) is the result of the S-normalization process.