Catalyzing Intelligent Logistics System Simulation with Data-Driven Decision Strategies

Machine learning is becoming an important technique in modern simulation systems due to the strong capability on capturing the random, complex, and dynamic features of the physical world. Based on these advantages, it has been employed as a powerful tool that enables the intelligent simulation of large-scale logistics systems in a highly efficient manner. Inspired by these applications, this work presents a new paradigm, where machine learning is utilized to generate data-driven decision strategies to accurately emulate the practical operations in logistics systems, and improve the simulation accuracy. Compared with existing approaches, the proposed method is also characterized by the high flexibility and transparency. Consequently, it can adapt to a large variety of logistics system architectures, and catch adequate details of system dynamics. Experiments have been conducted based on the simulation of large-scale real-world logistics systems, where the proposed method demonstrates superior accuracy on both strategy learning and simulation.