计算机工程与应用2017，Vol.53Issue(16)：62-67,171,7.DOI:10.3778/j.issn.1002-8331.1604-0271
基于改进粒子群算法的无水港多周期选址研究
Multi-stage dry port location model based on improved Particle Swarm Optimization(PSO).
摘要
Abstract
Traditional dry port location research often regard the dry port as a general logisticscenter, focusing on static location problem, failing to reflect dynamic planning process. Moreover, traditional Particle Swarm Optimization(PSO) algorithm is easy to fall into local optimum when dealing with discrete problems. A multi-stage dry port location model from the perspective of"mighty seaport"is established, with the objective function of maximizing total return. Transit ratio constraint and service time constraint are considered in the constraint condition. An improved PSO is designed to solve this problem. The result shows the exact locations of various stages. It is concluded that the improved algorithm enhances the local search ability and the global search ability. The feasibility and validity of the algorithm are also verified at the same time .关键词
粒子群算法/无水港/多周期/选址Key words
Particle Swarm Optimization(PSO)/dry port/multi-stage/location分类
信息技术与安全科学引用本文复制引用
汪传旭,陈倩,许长延..基于改进粒子群算法的无水港多周期选址研究[J].计算机工程与应用,2017,53(16):62-67,171,7.基金项目
上海市基础研究重点项目(No.15590501800) （No.15590501800）
上海海事大学博士创新基金资助项目(No.yc2012059). （No.yc2012059）
