## 2 Materials and Methods
### Evaluation Model Construction and Sensitivity Analysis
#### 2.3.2 Multi-Attribute Evaluation Index Sensitivity Analysis  
This research improved the method used for the sensitivity analysis of indexes and their weights, which was first created by [PERSON] and [PERSON]. In the actual evaluation, on the one hand, the method and results of the sensitivity analysis of the evaluation index values are combined to make key improvements and to optimize the corresponding indexes in accordance with the size of the changes in the evaluation parameters; this is carried out in order to improve the score of the supply chain comprehensive evaluation. On the other hand, the sensitivity analysis of the evaluation index weights is considered in order to guide other supply chain members in the fresh agricultural produce supply chain platform so that they can make targeted improvements as soon as possible. For example, for more sensitive weight indexes, supply chain members can take timely intervention measures to maintain the stability of the supply chain [35]. (1) Sensitivity analysis of evaluation indexes: Through the sensitivity analysis of fresh agricultural produce supply chain platform index values, supply chain members can clearly understand which index value changes will have a faster and greater impact on service quality evaluation results, and they will be able to obtain the improvement order of each index. Under the condition of limited resources being invested in the supply chain, decision makers in supply chain member companies can choose various measures to improve the service quality operation level in accordance with the sensitivity size; these measures can be targeted to improve the overall service quality of the supply chain. Combined with the sensitivity analysis, the index value \(Ai\) starts at 0 and changes to 100 with the step \(\Delta A^{\prime}=1\). According to the step index value score change \(\Delta A\), the target layer evaluation value score changes \(\Delta B\) accordingly, and the criterion layer evaluation value score changes \(\Delta C\) accordingly, meaning that the sensitivity \(\gamma_{a}\) of the comprehensive evaluation index value of the target layer can be expressed as follows:  
\[\gamma_{a}=\left|\frac{\Delta B/B}{\Delta A^{\prime}/A}\right| \tag{10}\]  
Combining the formula used to solve the index values and target tier score values with the weighted average method, the derivation of Equation (4) yields:  
\[\gamma_{a}=W*\Delta A*A/B \tag{11}\]  
The sensitivity \(\gamma_{b}\) of the evaluation index values in the criterion layer can be expressed as  
\[\gamma_{b}=\left|\frac{\Delta C/C}{\Delta A^{\prime}/A}\right| \tag{12}\]  
Combining the formula used to solve the index value and the score value of the criterion level with the weighted average method, the derivation of Equation (6), combined with the equation above, leads to  
\[\gamma_{b}=W*\Delta A*A/C \tag{13}\]  
As can be seen from Equation (7), the sensitivity of the index value change is mainly influenced by the combination of the index weight size, the initial index value, the index change value, and the initial score of the target layer (criterion layer). 1. Sensitivity analysis of index weights: small changes in the weights of the supply chain service quality evaluation indexes, which are constructed using the expert scoring method, may lead to large differences in the final evaluation and decision results. Once the sensitivity of the index weights or attribute values in the decision matrix is too large, the ranking results of the supply chain evaluation scheme will be comparatively very unstable, and the decision results will also be unstable. Through the sensitivity analysis of the evaluation results, regarding the weight of each index of the supply chain in terms of service quality, this research could help ascertain the influence of each index's weight. Consequently, this could help guide upstream members of the supply chain to coordinate and improve operations early and reduce subjective influences.