An AI-based Simulation and Optimization Framework for Logistic Systems

Improving logistics efficiency is a challenging task in logistic systems, since planning the vehicle routes highly relies on the changing traffic conditions and diverse demand scenarios. However, most existing approaches either neglect the dynamic traffic environment or adopt manually designed rules, which fails to efficiently find a high-quality routing strategy. In this paper, we present a novel artificial intelligence (AI) based framework for logistic systems. This framework can simulate the spatio-temporal traffic conditions to form a dynamic environment in a data-driven manner. Under such a simulated environment, it adopts deep reinforcement learning techniques to intelligently generate the optimized routing strategy. Meanwhile, we also design an interactive frontend to visualize the simulated environment and routing strategies, which help operators evaluate the task performance. We will showcase the results of AI-based simulation and optimization in our demonstration.