Data-Driven and Sustainable Transportation Route Optimization in Green Logistics Supply Chain

In the green logistics supply chain, transportation route optimization faces urgent problems such as high energy consumption and environmental pollution. This paper aims to achieve sustainable optimization of transportation routes through a data-driven approach. First, a large amount of transportation-related data is collected, including vehicle operation information, cargo characteristics, road conditions and meteorological factors. Big data analysis technology is used to clean and extract features from the data, and a transportation demand forecasting model is constructed. Then, a new optimization model is designed using the particle swarm optimization algorithm, with the goal of minimizing transportation costs and carbon dioxide emissions. In actual application cases, by optimizing the transportation routes of a logistics company, the results showed that the lowest transportation cost was 1,512 dollars and the lowest carbon dioxide emissions were 1.2 tons. Data-driven transportation route optimization not only improves logistics efficiency, but also promotes environmental sustainable development.