Resumen
In response to the evolving challenges of the integration and combination of multiple container terminal operations under berth water depth constraints, the multi-terminal dynamic and continuous berth allocation problem emerges as a critical issue. Based on computational logistics, the MDC-BAP is formulated to be a unique variant of the classical resource-constrained project scheduling problem, and modeled as a mixed-integer programming model. The modeling objective is to minimize the total dwelling time of linerships in ports. To address this, a Dueling Double DQN-based reinforcement learning algorithm is designed for the multi-terminal dynamic and continuous berth allocation problem A series of computational experiments are executed to validate the algorithm?s effectiveness and its aptitude for multiple terminal joint operation. Specifically, the Dueling Double DQN algorithm boosts the average solution quality by nearly 3.7%, compared to the classical algorithm such as Proximal Policy Optimization, Deep Q Net and Dueling Deep Q Net also have better results in terms of solution quality when benchmarked against the commercial solver CPLEX. Moreover, the performance advantage escalates as the number of ships increases. In addition, the approach enhances the service level at the terminals and slashes operation costs. On the whole, the Dueling Double DQN algorithm shows marked superiority in tackling complicated and large-scale scheduling problems, and provides an efficient, practical solution to MDC-BAP for port operators.