Dynamic Scheduling Model and Simulation Analysis of an AI-Driven Supply Chain Optimization Framework

The evolution of supply chains into complex networks necessitates intelligent and dynamic optimization methods. Although artificial intelligence (AI) shows significant potential, existing research often lacks integrated, reproducible models for full-chain dynamic decision-making. In this study the authors address this gap by proposing a novel AI-driven optimization framework. They formulate a Dynamic Demand Vehicle Scheduling Model as the core optimization problem and employ a genetic algorithm for its solution. Through comprehensive computational simulations comparing the AI-driven model against a traditional benchmark, the results demonstrate a superior performance of the smart supply chain, achieving 15-20% higher transportation volume, 30-40% reduction in delays, and 40-50% lower risk occurrence frequency. The genetic algorithm exhibits a more stable convergence trajectory compared with the baseline method. This study provides a verifiable methodology and quantitative evidence for AI applications in supply chains, offering significant theoretical and practical implications for intelligent transformation.