摘要
对配送方案的选择提出多目标优化,在满足客户需求的前提下,力求成本最低和各配送中心负荷均衡,建立多目标规划模型。运用粒子群算法对解空间粒子进行局部和全局的搜索,再运用自适应网格算法对非劣解外部集进行更新和维护,保持其规模。实证表明,采用基于自适应网格的多目标粒子群算法对该模型进行求解能够得到均匀分布于解空间的Pareto前沿。结果表明两目标具有一定的悖反关系,据此选择满意解。
Multi-objective optimization was advanced for selection of distribution solutions. A multi-objective planning model was built on the demand of customers to achieve both the lowest cost and burden level equilibrium of each distribution center. Particle swarm optimization （PSO） algorithm was used for both local and global search in solution space. Adaptive grid algorithm （AGA） was used to update the non-inferior solutions archive and maintain its dimension. The demonstration indicates that a Pareto front which evenly distributed in the solution space can be obtained by using multi-objective particle swarm optimization algorithm based on adaptive grid algorithm. The result shows a kind of contrary relationship between these two objectives. Thus a satisfactory solution can be found.
出处
《公路交通科技》
CAS
CSCD
北大核心
2010年第5期132-136,共5页
Journal of Highway and Transportation Research and Development
基金
国家自然科学基金资助项目(70772073)
上海市自然科学基金资助项目(07ZR14003)
上海市社科规划基金资助项目(2007BZH001)
关键词
运输经济
配送方案
自适应网格粒子群算法
多目标优化
transport economics
distribution solution
adaptive grid particle swarm optimization algorithm
multi-objective optimization
