基于复杂网络理论的供应链研究

Supply chain research based on complex network theory

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摘要供应链是由原材料供应商到最终客户的一系列生产和流通过程所形成的链条结构,连接上、下游各个企业之间的关系.供应链包含采购、生产、仓储、分销、客户服务、信息管理和资金管理等不同环节之间的物料、资源、资金和信息等的相互关联流动,从而形成复杂的网链结构.高效可靠的供应链对于增强企业的市场竞争力、推动社会和经济持续发展具有重要意义.近年来,采用复杂网络的理论和方法对供应链进行建模和分析受到了越来越多学者的关注.本文系统性地论述基于实证数据和网络模型两种类型的供应链网络构建研究,在此基础上探讨供应链网络的拓扑结构、脆弱性、关键节点识别、社团检测等结构性质,阐明供应链网络的风险传播、竞争博弈等管理特性.本文综述了基于复杂网络方法的供应链研究内容和前沿热点,论证了复杂网络理论在供应链网络研究中的有效性和适用性,分析了供应链网络研究现存的一些问题,并展望若干未来的重要研究方向.本文期望为供应链研究提供见解,并推动复杂网络方法在供应链研究中的发展和应用. Supply chain is a chain structure formed by the sequential processes of production and distribution,spanning from raw material suppliers to end customers.An efficient and reliable supply chain is of great significance in enhancing enterprise's market competitiveness and promoting sustainable social and economic development.The supply chain includes the interconnected flows of materials,resources,capital,and information across various stages,including procurement,production,warehousing,distribution,customer service,information management,and financial management.By representing the various participants in the supply chain as nodes and their interactions—such as the logistics,capital flow,information flow,and other interactions—as edges,the supply chain can be described and characterized as a complex network.In recent years,using complex network theory and methods to model and analyze supply chains has attracted increasing attention from researchers.This paper systematically reviews the supply chain research based on complex network theory,providing an in-depth analysis of supply chain networks in terms of network construction,structural properties,and management characteristics.First,this paper reviews two kinds of approaches to constructing supply chain network:empirical data-based approach and network model-based approach.In the empirical data-based research,scholars use common supply chain databases or integrate multiple data sources to identify the supply chain participants and clarify their attributes,behaviors,and interactions.Alternatively,the research based on network models employs the Barabási-Albert(BA) model,incorporating factors such as node distance,fitness,and edge weights,or uses hypergraph models to construct supply chain networks.Next,this paper summarizes the research on the structural properties of supply chain networks,focusing on their topological structure,key node identification,community detection,and vulnerability analysis.Relevant studies explore the topological structure of supply chain networks,uncovering the connections between nodes,hierarchical structures,and information flow paths between nodes.By analyzing factors such as node centrality,connection strength,and flow paths,the key nodes within the supply chain network are identified.Community detection algorithms are used to investigate the relationships between different structural parts and to analyze the positional structure,cooperative relationships,and interaction modes.Furthermore,quantitative evaluation indicators and management strategies are proposed for the robustness and resilience of supply chain networks.Further research has explored the management characteristics of supply chain networks,including risk propagation and competition game.Relevant studies have employed three main methods—epidemic model,cascading failure model,and agent-based model—to construct risk propagation models,simulate the spread of disruption risks,and analyze the mechanisms,paths,and extent of risk propagation within supply chain networks.These studies provide valuable insights for developing risk prevention and mitigation strategies.In addition,the game theory has been used to investigate the cooperative competition,resource allocation,and strategy selection among enterprises within the supply chain network.This paper reviews the research contents and emerging trends in supply chain studies based on complex network methods.It demonstrates the effectiveness and applicability of complex network theory in supply chain network research,discusses key challenges,such as how to obtain accurate,comprehensive,and timely supply chain network data,proposes standardized data processing methods,and determines the attributes of supply chain network nodes and the strength of their relationships.Furthermore,research on the structure of supply chain network has not yet fully captured the unique characteristics of supply chain networks.Existing models and methods for vulnerability assessment often fail to consider the dynamic and nonlinear characteristics of supply chain networks.Research on risk propagation in supply chains has not sufficiently integrated empirical data,overlooking the diversity of risk sources and the complexity of propagation paths.The asymmetry and incompleteness of information in supply chain networks,as well as multiple sources of uncertainty,make the prediction and analysis of multiparty decision-making behavior more complex.This paper also outlines several key directions for future research.One direction involves using high-order network theory to model interactions among multiple nodes and to describe the dynamics of multi-agent interactions within supply chain networks.Furthermore,integrating long short-term memory(LSTM) methods to process long-term dependence in time-series data can enhance the analysis of network structure evolution and improve the prediction of future states.The application of reinforcement learning algorithms can also adaptively adjust network structures and strategies according to changing conditions and demands,thereby improving the adaptability and response speed of supply chain networks in emergency situations.This paper aims to provide valuable insights for supplying chain research and promoting the development and application of complex network methods in this field.

作者崔俊英徐舒琪那旭潘黎明吕琳媛 Cui Jun-Ying;Xu Shu-Qi;Na Xu;Pan Li-Ming;LüLin-Yuan(Department of Physics,University of Fribourg,Fribourg CH-1700,Switzerland;Institute of Dataspace,Hefei Comprehensive National Science Center,Hefei 230088,China;Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 611731,China;School of Cyber Science and Technology,University of Science and Technology of China,Hefei 230026,China)

机构地区瑞士弗里堡大学物理学系合肥综合性国家科学中心数据空间研究院电子科技大学基础与前沿研究院中国科学技术大学网络空间安全学院