# A Review of Supply Chain Resilience: A Network Modeling Perspective
## 3 Current Status of Supply Chain Network Modeling Research
### Agent-Based Modeling of Supply Chain Networks  
The agent-based modeling (ABM) approach treats each entity within a system as an independent agent, defining the behaviors of each individual and its interactions with the environment to simulate and analyze the emergent behaviors of the entire system [66]. This modeling method is well-suited for simulating complex systems, especially those involving multiple autonomous decision-makers. The multi-agent modeling approach offers a high degree of flexibility and scalability, enabling analysis at both the micro and macro levels. The analysis can either focus on the behavior of an individual agent or explore the collaboration between multiple agents, as well as the collective behavior of the system. Its core principle is \"divide and conquer,\" where a complex system is decomposed into several relatively independent subsystems. Through the collaboration of agents, the overall system can achieve optimization and accomplish tasks [67]. Agent-based network modeling has been under development for an extended period. With the rise of deep reinforcement learning, this approach has regained significant attention in the academic community. A search in the databases \"SCI-E\" and \"SSCI\" using the query TS = \"supply chain\" AND TS = \"agent\" yielded 1378 relevant articles after filtering out the unrelated literature. These articles were analyzed using CiteSpace, resulting in a research hotspot evolution analysis, as shown in Figure 8. Keywords such as \"multi-agent systems\", \"simulation\", \"agent-based systems\", and \"supply chain management\" have exhibited notable citation bursts over the past few years, highlighting their importance in research on supply chain resilience. A supply chain network, as a complex, multi-layered, and multi-faceted system, is far more than a simple linkage of multiple enterprises. It consists of numerous entities with autonomous decision-making capabilities, information transmission abilities, and interaction mechanisms. Within this network, each node, such as suppliers, manufacturers, and distributors, can be viewed as an agent with independent decision-making, behavioral capabilities, and the ability to interact with other nodes [46]. By simulating and analyzing the behaviors and collaborative patterns of these agents, researchers can gain deeper insights into the complex dynamic processes, information flows, and resource allocation within supply chain networks. At the micro level, agent-based modeling of supply chain networks primarily focuses on modeling individual behaviors and decision-making processes. As seen in Figure 8, with keywords such as \"coordination\" and \"decision making\", this line of research emphasizes analyzing the interaction patterns, coordination mechanisms, and how agents make decisions based on their goals and the environment. These studies have been widely applied to  
Figure 7: (**a**) Two-layer epidemic model considering information propagation and (**b**) possible state transitions in the model [56]. optimize tasks such as resource allocation, inventory management, route planning, and scheduling within supply chains. For instance, [PERSON] et al. [68] proposed an ontology-based multi-agent decision support framework (Figure 9) to address data heterogeneity issues in the prefabricated component supply chain, aiming to enhance decision-making and resource allocation efficiency. Additionally, [PERSON] et al. [69] investigated firms' adaptive strategies in response to disruptions within supply chain networks using an agent-based model, exploring the roles of reactive and proactive strategies for mitigating the impact of supply chain interruptions. By simulating various supply chain scenarios, researchers can test and validate different decision-making mechanisms to optimize operational efficiency at each stage, thereby improving the performance of the overall system.