集装箱码头班轮船期准班率的优化  

Optimization research on vessel schedule reliability in container terminal

作　　者：罗勋杰 吴德烽[1] 马孜[1] 刘水国[3] 胡艳君[4] 

机构地区：[1]大连海事大学自动化研究中心,辽宁大连116026 [2]青岛前湾集装箱码头有限公司,山东青岛266500 [3]青岛港湾职业技术学院,山东青岛266404 [4]武汉交通职业学院,武汉430065

出　　处：《大连海事大学学报》2010年第2期58-61,共4页Journal of Dalian Maritime University

基　　金：辽宁省科技厅基金资助项目(2007219003)

摘　　要：为提高集装箱码头班轮船期准班率,提出基于动态泊位分配的优化数学模型.在考虑桥吊分配前提下,该模型以船舶离港实际开航时间与船期表要求开航时间的平均绝对时间差最小为优化目标,分别应用微粒群算法(PSO)和遗传算法(GA)求解该数学模型。仿真结果表明:两种算法均可使船期延误时间减少40%;PSO算法较GA算法具有更快的收敛速度.To improve the vessel schedule reliability in container terminal,the optimization model based on dynamic berth allocation was proposed.Considering the quay crane allocation,the model focused on the minimum average schedule missed hours of vessel between the departure time of vessel schedule and the actual departure time,and particle swarm optimization（PSO） algorithm and genetic algorithm（GA） were used respectively to calculate the optimization model.Simulation results show that both PSO and GA are performed well and average schedule missed hours are reduced by 40%,and PSO has faster convergence rate than that of GA.

关 键 词：集装箱码头 班轮 准班率 微粒群算法(PSO) 遗传算法(GA) 

分 类 号：U691.31[交通运输工程—港口、海岸及近海工程]