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.

Key words: Particle Swarm Optimization（PSO）, dry port, multi-stage, location

摘要： 传统无水港选址主要将无水港当作一般物流中心，侧重研究静态问题，难以体现选址动态规划特征，且传统粒子群算法在处理离散问题时易陷入局部最优困境。因此从“强势海港”角度，构建了基于收益最大化的无水港多周期选址。模型考虑了无水港的中转比例约束和服务时间约束，然后运用改进的粒子群算法进行了求解，得出了各阶段选址结果。表明改进算法的局部搜索能力和全局搜索能力都得到增强，算法的可行性和有效性也得到了验证。

关键词: 粒子群算法, 无水港, 多周期, 选址