Genetic Algorithm-Based Intelligent Optimization for Enhancing Security in High-Performance Concrete Supply Chains

Abstract

The supply chain security of high-performance concrete materials directly affects the orderly progress of engineering projects. At present, its supply chain management still faces problems such as high costs, insufficient timely supply, and poor quality stability. This article aims to construct an intelligent optimization design algorithm model to enhance the safety and management efficiency of the high- performance concrete material supply chain. In terms of methodology, firstly, based on the principles of scientificity, comprehensiveness, and operability, a supply chain security indicator system covering supply, production, transportation, and other links should be established. Subsequently, based on genetic algorithm (GA), the model improvement design was completed by optimizing the encoding method, adjusting the fitness function, and improving the genetic operator. The results were compared and verified with traditional methods through experiments. The key results show that the optimized model has an average cost lower than traditional methods by about 170200 yuan, an increase in on-time delivery rate of about 9.92%, and an increase in product qualification batch rate of about 3.1%. The conclusion indicates that the intelligent optimization design algorithm model can effectively optimize the supply chain management of high-performance concrete materials, providing reliable support for their supply chain security.