Using Dynamic Planning Algorithm to Solve Truck Scheduling Problems in Intelligent Logistics

Abstract

This study investigates the application of dynamic planning algorithms to address truck scheduling challenges within intelligent logistics systems. The research focuses on evaluating the effectiveness, efficiency, and applicability of dynamic programming and reinforcement learning algorithms in optimizing truck scheduling operations. Through a structured experimental setup, statistical analysis is conducted to assess solution quality, computational efficiency, convergence behavior, and sensitivity to input parameters. Results reveal a notable 15% reduction in transportation costs achieved by dynamic programming, highlighting its robustness and reliability in finding near-optimal solutions. Meanwhile, reinforcement learning algorithms demonstrate promising performance in balancing solution quality and computational efficiency, albeit with variable convergence behavior and sensitivity to parameter tuning. The discussion underscores the importance of algorithm selection, parameter tuning, and problem formulation in achieving optimal performance in truck scheduling optimization. Overall, this study contributes valuable insights into the strengths, limitations, and trade-offs associated with dynamic planning algorithms for truck scheduling in intelligent logistics systems, informing decision-makers and practitioners about the most suitable approaches for enhancing logistics operations efficiency.