摘要
将实际的物流配送网络描述为由配送中心和顾客两类节点构成的不完全无向图,并采用模糊数表示车辆行驶时间和顾客服务时间的不确定性,建立了物流配送车辆路径优化的模糊规划模型。为了求解上述模型,首先将模型进行清晰化处理,使之转化为一类确定性多设施车辆路径模型,然后设计了嵌入FLOYD算法的捕食搜索算法对之进行求解。通过仿真实例计算,并与遗传算法比较,取得了满意的结果。
The logistics distribution networks were described in a way of an incomplete undigraph, which consisted of two kinds of nodes, the distribution center nodes and the customer nodes. A fuzzy programming model was built to optimize logistics distribution vehicle routing problem, where vehicle travel time and customer service time are fuzzy. The model was firstly converted into a crisp multi-depot vehicle routing problem, and then it was solved by a predator search algorithm with FLOYD. Computation on simulation examples and comparison with genetic algorithm show the model and algorithm are effective.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2006年第11期3301-3304,3312,共5页
Journal of System Simulation
基金
国家自然科学基金重点资助项目(70431003)。
关键词
物流配送
车辆路径
模糊规划
FLOYD
捕食搜索算法
logistics distribution
vehicle routing problem
fuzzy programming
FLOYD
predatory search algorithm