Optimization approach of berth-quay crane-truck allocation by the tide, environment and uncertainty factors based on chaos quantum adaptive seagull optimization algorithm

Abstract

The post-epidemic era has led to the accumulation of cargo, which has brought greater pressure to container ports. Since traditional methods cannot simultaneously consider the effect of tidal, uncertain, and environmental factors on the allocation plan. To relieve this pressure, firstly, considering tidal factors, formulating time window rules, thinking out uncertain factors, and determining constraints from three perspectives of vessel berthing process, quay crane and container truck operation, a new berth-quay crane-truck joint scheduling model is constructed by minimizing three aspects of vessels turnaround time, the carbon emissions of quay cranes and trucks, namely TEU-BQCT model. Then, aiming at obtaining a relatively high-quality solution, combining chaotic mapping and quantum entanglement, a new chaotic quantum adaptive seagull optimization algorithm is proposed, namely CQASOA, exclusive coding rules suitable for the TEU-BQCT model is formulated, a feasible integer algorithm is developed, the external penalty function is constructed to limit constraints, and a novel joint scheduling solution method of berth-quay crane-truck is proposed, namely TEU-BQCT_CQASOA. Subsequently, two ports of different scales in South China are used to test the constructed solution method feasibility. The simulation results indicate that the constructed TEU-BQCT model can obtain a more suitable scheduling scheme. The proposed CQASOA has better performance than other comparison algorithms selected in this paper, which can obtain a better solution when solving the TEU-BQCT model.