摘要
根据B2C(商家对客户)电子商务环境下物流配送的特点建立了带预约时间的车辆路径问题(VRP)数学模型,设计了求解多目标优化的蚁群算法,各个目标具有相同的重要性.在蚁群的状态转移概率中引入预约时间窗宽度及车辆等待时间因素,记录优化过程中产生的Pareto最优解,用Pareto最优解集来指导蚁群的信息素更新策略.采用改造的Solomon数据进行仿真实验,用Solomon最优解与本文的结果进行比较,实验结果验证了模型的合理性及算法的有效性.
According to characteristics of logistics distribution in B2C（business to customer） electronic commerce, a mathematical model about vehicle routing problem（VRP） with time reservation is developed. An ant colony algorithm for solving multi-objective optimization is designed. Each objective has the same importance. The algorithm introduces factors of booking time window width and vehicle waiting time into state transfer rules, and records Pareto optimal solution generated in the optimal process. Pareto optimal set is employed to guide the pheromone-updating tactics. Improved Solomon data are adopted in emulation experiments. Solomon optimal solution is compared with the result of emulation experiments. Experiment results show the rationality of the proposed model and the effectiveness of the algorithm.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2011年第1期87-93,共7页
Control Theory & Applications
基金
国家自然科学基金资助项目(70771020
70721001)
国家"863"计划/先进制造技术领域专题项目(2007AA04Z194)
教育部新世纪优秀人才支持计划(NCET-06-0286)
关键词
B2C电子商务
车辆路径问题
多目标优化
PARETO最优解
时间窗
蚁群算法
B2C electronic commerce
vehicle routing problem
multi-objective optimization
Pareto optimal solution
time windows
ant colony algorithm
