A supply chain network resilience prediction optimization method fusing machine learning and evolutionary algorithm

Abstract

The application discloses a supply chain network resilience prediction optimization method fusing machine learning and an evolutionary algorithm, and comprises the following steps: constructing a multi-layer dependent supply chain network model and a hybrid cascading failure model; secondly, multi-scene simulation is carried out on the model, the resilience evaluation index and the resilience value sequence based on a full-role subgraph are obtained, and a training sample is formed; then, a graph neural network model fusing a GraphSAGE framework and a GAT multi-head attention mechanism is constructed, and the model is trained by using the training sample; finally, the supply chain network structure data to be evaluated and an attack scene are input into the trained graph neural network model, and a node-level failure probability is quickly predicted. The application greatly improves the efficiency of supply chain network resilience optimization under the premise of ensuring accuracy by fusing the rapid prediction capability of machine learning and the optimization capability of an evolutionary algorithm, and provides effective support for risk management and resilience improvement of a complex supply chain network.

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