Integrated scheduling of cargo vessels, research vessels, and marine experiments in multifunctional ports using Q-learning enhanced PSO

• A hierarchical spatiotemporal scheduling mechanism is designed to resolve conflicts between vessels and marine experiments. • The novel BCAEA model is constructed to minimize the vessel turnaround time, crane movement distance, and experiment completion time. • The QLEPSO algorithm is proposed by integrating the position update strategy pool, Q-learning strategy selection, and adaptive parameter control. • The BCAEA_QLEPSO method is established and compared with FCFS to verify its effectiveness and superiority. Multifunctional ports integrating cargo and research operations (CRPs) face unprecedented scheduling complexities due to spatiotemporal conflicts among cargo vessels, research vessels, and marine…

• NN
  National Natural Science Foundation of China
• NS
  Natural Science Foundation of Hainan Province
• KR
  Key Research and Development Project of Hainan Province