Abstract: To improve the logistics service efficiency and service level of port firm,an optimal planning model of stockpiling service for the containers yard area was built,and a discrete Dynamic Multi-objective optimization Particle Swarm Optimization algorithm with Constraints (CMDPSO) was developed to solve this kind of problems.The case study showed that the proposed model and algorithm could not only effectively increase the utilization rate of container yard's space resources,but also decrease the turning-box rate and the transportation burden between the container yard and the port.Thus the logistics service resource was saved,the operating cost was reduced and the service level of ports was improved.

Key words: port, container yard, stockpiling service capability planning model, particle swarm optimization algorithm

摘要： 为了提高港口企业的物流服务效率和服务水平,以其场站堆场堆存服务为研究对象,建立了堆场堆存服务计划优化模型,并设计了带约束的离散多目标动态优化粒子群算法来求解该类问题。实验结果表明,所提模型和算法不但能够有效提高场站堆场空间资源利用率,而且能够有效减少箱区翻箱以及场站－码头双向运输负荷,从而既节省了物流服务资源,降低了港口企业运营成本,又能提供较好的港口服务水平。

关键词: 港口企业, 场站堆场, 堆存服务能力计划模型, 粒子群优化算法