# A Review of Supply Chain Resilience: A Network Modeling Perspective
###### Abstract  
Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the theoretical foundations and research progress in supply chain resilience, focusing on the application of network modeling methods. First, the concept of supply chain resilience is defined, and its developmental trajectory is reviewed. Through literature visualization analysis, this study delves into the current state of research on supply chain resilience, addressing challenges and risk management, highlighting the importance of network modeling techniques in this field. Subsequently, it explores supply chain network modeling based on complex networks and agent-based modeling, analyzing their strengths and limitations in simulating the overall evolution of supply chains and the dynamic behavior of individual entities. By integrating network structural characteristics with resilience evaluation methods, this paper suggests potential directions for future research. These include enhancing the description of individual firm behavior, analyzing the dynamics of information networks, and emphasizing task-oriented model design, thereby offering new perspectives and pathways for managing supply chain resilience in a way that can generate significant positive externalities for global economies. This research also indicates that the enhanced resilience of supply chains can produce a multiplier effect, benefiting not only individual firms but also promoting economic stability and growth across multiple countries. supply chain resilience; network modeling; complex networks; multi-agent systems; literature visualization analysis  
Current research on supply chain network resilience predominantly focuses on static analyses or evaluations at specific time points, often overlooking the dynamic evolution of supply chains over time. As a result, there is a lack of in-depth studies on how resilience changes across different time scales. To address this gap, it is crucial to develop dynamic models to better capture the resilience fluctuations throughout the supply chain's life cycle. Moreover, existing resilience assessment methods tend to consider a limited range of factors, primarily focusing on operational aspects while neglecting broader influences, such as policies, market conditions, and technological advancements. To provide a more holistic evaluation, future research should integrate these diverse factors and expand the scope to include financial and market resilience, offering a comprehensive view of the supply chain's overall resilience. Additionally, there is a noticeable scarcity of cross-industry and context-specific applications of resilience evaluation methods. So, it is necessary to apply these models to various industries and investigate the resilience of supply chains under unique scenarios, such as political conflicts or natural disasters, to refine strategies for coping with such challenges. In this context, we propose a new idea that combines complex network theory with agent-based modeling, offering a more comprehensive and dynamic framework for supply chain network modeling. This innovative concept enables the analysis of the supply chain's structure, information flow, and node connectivity at a macro level while simultaneously simulating the autonomous behavior and decision-making processes of individual entities at the micro level. This dual approach not only improves the model's accuracy but also enables a deeper understanding of interactions and coordination during task execution, especially in uncertain and complex environments. By utilizing agent-based modeling, we can uncover the behavioral characteristics and decision-making logic of individual nodes. In contrast, complex network modeling provides insights into the efficiency, paths, and topology of information transmission.