# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 4 Resilience Assessment Methods: Current Research Status  
### Traditional Resilience Assessment Methods  
The process of resilience assessment can be categorized into two main types: qualitative description and quantitative description [74]. Qualitative descriptions typically focus on further refining and elaborating the concept of resilience. Quantitative descriptions, on the other hand, can be divided into two subtypes: specialized assessments tailored to specific domains and general assessments applicable across systems. Specialized resilience assessments are designed for the structural characteristics of specific systems and lack broad applicability. Therefore, this paper does not delve into these in detail. General resilience assessment refers to constructing a universal evaluation framework that uses system performance data as input to produce quantitative results. These metrics allow for intuitive comparisons of resilience levels between different systems or within the same system under varying conditions [75]. Since the results of general assessments rely solely on system performance data and are independent of the specific structure of the system, they can be widely applied to resilience analyses across diverse systems. In the field of resilience metrics research, [PERSON] et al. [76] were the first to propose a triangular model (Figure 11) to construct a resilience assessment framework for analyzing the impacts of earthquakes on community losses. The resilience calculation method is detailed in Equation (1). \[RL=\int_{t_{0}}^{t_{1}}{[100-Q(t)]dt} \tag{1}\]  
Here, \(t_{0}\) represents the moment when the disturbance occurs, \(t_{1}\) marks the time when the system recovers to normal, and \(Q(t)\) denotes the system's quality during the disturbance period. \(Q(t)\) can represent various performance metrics. A higher \(RL\) value indicates lower resilience, whereas a smaller \(RL\) value reflects higher resilience. The assumptions underlying this method have been a topic of debate. First, setting the initial performance state of all systems uniformly to 100 may be problematic, as the normal operating state of different systems is difficult to quantify accurately as 100. Second, the assumption that system performance immediately drops to its lowest point upon disturbance is not universally applicable, as some systems experience gradual declines in performance. Third, the assumption that recovery actions commence immediately after a disturbance may not be entirely realistic, as certain systems may require an adaptation period before recovery efforts begin. Finally, the assumption that system performance can fully recover to its original state is also questionable, as some systems may struggle to return to their pre-disturbance performance levels. [PERSON] et al. [78] proposed a hypothesis addressing the aforementioned deficiencies, suggesting that a system undergoes three distinct steady states during a single disturbance or attack, as illustrated in Figure 12. The first stage is the initial steady state, representing the system's condition before the disturbance (from \(t_{0}\) to \(t_{e}\)). The second stage is the post-disturbance steady state, which reflects the state of the system under the influence of the disturbance (from \(t_{d}\) to \(t_{s}\)). The third stage is the recovered steady state, indicating the new state the system reaches after recovery operations are completed (after \(t_{f}\)). Some studies have conducted an in-depth exploration of resilience quantification metrics to more comprehensively evaluate system resilience. [PERSON] et al. [79] categorized resilience into two dimensions: robustness and adaptive capacity. They defined system resilience as the ratio of system strength to the applied load, with the calculation formula shown in Equation (2). \[R_{e}=\frac{T_{i}+F\Delta T_{f}+R\Delta T_{r}}{T_{i}+\Delta T_{f}+\Delta T_{r}} \tag{2}\]  
Here, \(T_{i}\) represents the time when the event occurs, \(T_{f}\) denotes the time of failure, and \(T_{r}\) indicates the recovery time.