基于改进 D3QN 的煤炭码头卸车排产智能优化方法

Intelligent decision scheduling can improve the operation efficiency of large ports, which is one of the important research directions for the implementation of artificial intelligence technology in the smart port scenario. This article studies the intelligent unloading scheduling tasks of coal terminals and abstracts them as a Markov sequence decision problem. A deep reinforcement learning model for this problem is established, and an improved D3QN algorithm is proposed to realize intelligent optimization of unloading scheduling decisions by considering the characteristics of high action space dimension and sparse feasible action in the model. The simulation results show that for the same set of random task sequences, the optimized scheduling strategy obviously improves the efficiency compared with the random strategy. At the same time, the trained scheduling strategy is directly applied to the randomly generated new task sequence, and the scheduling efficiency is improved by 5%~7%, which indicates that the optimization method has good generalization ability. In addition, with the increasing complexity of decision models, traditional heuristic optimization algorithms are faced with prominent problems such as difficult modeling and low solving efficiency. This article provides a new idea for studying this kind of problem, which is expected to realize the wider application of deep reinforcement learning-based intelligent decision-making in port scheduling tasks.