摘要
针对随机需求下带时间窗的存贮路径问题,建立了多目标库存和配送策略优化模型,用多目标遗传算法对模型求解.该算法采用精华保留策略和自适应调整策略等遗传算子逼近全局最优解,可以克服遗传算法局部搜索能力不足的缺陷,提高收敛速度和改善全局寻优性能.以某物流公司的产品配送系统为例,用多目标遗传算法获得了费用较低的方案.
An optimization model for multi-objective inventory and distribution strategies was established to solve the stochastic demand inventory routing problem with time windows （IRPTW）. The model was solved with a multi-objective genetic algorithm （GA）. The algorithm uses such genetic operators as best choice and adaptive strategy to approach the global optimal solution. It overcomes the inability of conventional GA in local search, increases convergence speed, and improves global optimization performance. A product distribution system of a logistic company was taken as an example, and the result shown that an optimal scheme with a reasonably low cost was obtained with the algorithm.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2009年第2期289-294,共6页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(70271022)
高等学校博士学科专项科研基金资助项目(20030613016)
关键词
多目标遗传算法
随机需求
时间窗
IRP
优化
multi-objective genetic algorithm
stochastic demand
time window
inventory routingproblem
optimization
