Transportation and Logistics in the Generative AI Era: Constructing the GAIL-SCN (Generative Artificial Intelligence Logistics–Supply Chain Nexus) Theory

The rapid development of generative artificial intelligence (GAI) is profoundly changing the operational model of the transportation and logistics industry. Traditional logistics systems primarily rely on human experience, fixed rules, and historical data for decision-making, while GAI can achieve dynamic collaboration and continuous optimization of logistics systems through real-time data analysis, predictive generation, intelligent optimization, and autonomous decision-making. This paper proposes a new theoretical framework—GAIL-SCN (Generative Artificial Intelligence Logistics-Supply Chain Nexus)—based on complex systems theory, intelligent logistics theory, and digital supply chain theory. This theory posits that GAI forms a closed-loop intelligent logistics ecosystem by connecting data flow, information flow, logistics, and value flow. This paper constructs the theoretical model, operational mechanism, and mathematical expressions, and analyzes its application value in smart transportation, warehouse management, supply chain collaboration, and green logistics. Research shows that GAI is driving the transformation of logistics systems from "information-driven" to "intelligent generation-driven," providing a new theoretical foundation for the future development of smart transportation and the digital economy.