Analyzing Supply Chain Network Optimization in Digital Trade Using Graph Algorithms

In response to the complex problems of path planning, infrastructure optimization and logistics flow management in digital supply chain networks, this paper introduces graph theory algorithms such as Dijkstra algorithm, Kruskal algorithm, and Edmonds-Karp algorithm, aiming to improve logistics efficiency, reduce operating costs, and enhance the supply chain's ability to cope with uncertainty. First, in the transportation network, each node represents a storage or production point, and the edge weight represents the transportation time or cost. This paper uses the Dijkstra algorithm to optimize the logistics path by calculating the shortest path between supply chain nodes. Then, the Kruskal algorithm is used to design the optimal infrastructure layout of the supply chain. The algorithm constructs a minimum spanning tree based on the weight of the edge, avoiding redundant paths while ensuring good connections between nodes, thereby improving the robustness and resource utilization efficiency of the supply chain. Finally, the Edmonds-Karp algorithm is applied to optimize the logistics flow of the supply chain. The algorithm solves the maximum flow problem to ensure smooth logistics flow between various links in the logistics network, reduce the occurrence of bottleneck problems, and improve the overall logistics capabilities of the supply chain. Through the Dijkstra algorithm, transportation time is reduced by 25% and logistics costs are reduced by 20%; through the Kruskal algorithm, infrastructure costs are reduced by 30%; after applying the Edmonds-Karp algorithm, the supply chain logistics flow has been increased by 35%, and logistics bottlenecks have been reduced by 40%. Through experimental verification, the graph algorithm proposed in this article can significantly improve the efficiency of the supply chain, reduce costs, and show good sustainability when considering green supply chain goals.