FSCO: A Secure and Adaptive Framework for Supply Chain Optimization

With the rapid growth of the e-commerce industry, the demands on logistics and transportation for timeliness and efficiency are increasing. Traditional route optimization methods may struggle with real-time traffic, configuration, and network changes, requiring more adaptive solutions. This paper proposes a novel route optimization methodology, FSCO, integrating genetic algorithms, KMeans clustering, artificial intelligence algorithms, federated learning, and blockchain technology. Genetic algorithms provide a comprehensive exploration of search spaces to identify cost-effective routes. KMeans clustering optimizes route selection by analyzing traffic data, allowing the system to adapt to real-time changes. Artificial intelligence algorithms enhance responsiveness through real-time predictions and adjustments. Federated learning enables multiple nodes to collectively optimize the dataset while preserving privacy, achieving complete data decentralization. Blockchain technology ensures data security through immutability and transparency, preventing disruptions and unauthorized manipulations. This paper details the system architecture and its operational mechanisms, highlighting key aspects and advantages, and demonstrating significant potential in addressing dynamic route adaptation, data privacy, and information security in logistics.