Collaborative scheduling of port crane clusters based on the combination of dual-cycle model and hybrid particle swarm optimization

Port multi-type cranes can improve operational efficiency and reduce energy consumption through reasonable scheduling operations, most of the problems faced by the current port crane operation process is mainly difficult to ensure that the crane in the working process to maintain a full load operating state. Therefore, this study proposes a double-cycle scheduling model based on the work of multiple types of cranes in the port, the model by reasonably allocating different types of cranes to carry out reasonable operations, so as to enhance the efficiency of cranes in the port and the yard between the work. The optimized scheduling aims to shorten the total waiting time of transport cranes and ensure their high workload efficiency. In addition, the model also considers the randomness of the operating time of harbor cranes to ensure that it fits the actual working situation. Therefore, this study uses a hybrid Cauchy variational and historical information probabilistic compensation particle swarm optimization algorithm (CHPSO) combined with a double-loop scheduling model to optimize the total waiting time of transport cranes. The experimental results show that this method can reduce the total waiting time of transportation cranes and significantly improve the efficiency of multi-type cranes in ports.