A container port berth allocation optimization method based on the reverse learning BWO algorithm

With the rapid development of global shipping,port cargo volumes are increasing significantly,leading to growing issues of vessel congestion and delays,which in turn severely constrain port operations. In response to the challenges posed by surging cargo volumes,ship congestion,and aggravated pollution,this paper proposes a Time-Berth ＆Pollution（ TB＆P） model. The model takes the difference between the actual and expected time in port as the objective function,aiming to minimize operating costs and pollution emissions,subject to constraints related to time,space,and machinery/equipment. To solve the TB＆P model,an improved version of the basic Beluga Whale Optimization（ BWO）algorithm is developed,termed the Opposition Learning Beluga Whale Optimization（ OBWO） algorithm. The feasibility and superiority of the proposed model and improved algorithm are verified through case data from a port. Results demonstrate that,compared with traditional models,the established model significantly reduces the extent of ship delays and mitigates water pollution in the port area. Furthermore,the proposed OBWO algorithm exhibits enhanced stability and accuracy relative to other selected algorithms.