摘要
针对冷链物流末端配送系统中顾客需求随机、需求种类及温层多样的问题,构建了随机需求下具有配送时限要求的冷链品多温共配路径优化模型,设计了集K-means聚类算法、蚁群算法和随机动态规划算法为一体的路径优化算法,使所有可能路径的期望配送时间(包括前行/回程补货时间)满足配送时限要求。最后,通过算例验证了数学模型及算法的有效性,并对配送时限、车容量等参数进行了灵敏度分析,结果表明:配送时间与配送时限、车容量呈负向关系;随着时限的不断放宽,期望配送时间不再减少,此时,需要增加车辆容量,以减少回程补货次数。
With consideration of the consumption characteristics of stochastic, small volume and large variety, a programming model of Multi-temperature Joint Distribution with stochastic demands and time limit is established. The optimization algorithm of K-means clustering algorithm,ant colony algorithm and stochastic dynamic programming algorithm are designed to guarantee the expected time（including forward or return replenishment time）of each path meet the time limit. Finally, a numerical example is adopted to demonstrate the validity of the model and algorithm. Sensitivity of the relevant parameters（including the time limit and the capacity of vehicle）is analyzed. The results show that there are negative relationships between delivery time and time limit/vehicle^s capacity while the expected delivery time would no longer reduce if the time limit exceeds a certain limit. At this point, the company may reduce return replenishment time by increasing the traffic capacity.
出处
《工业工程与管理》
CSSCI
北大核心
2016年第2期49-58,共10页
Industrial Engineering and Management
基金
国家自然科学基金资助项目(71372122)
山东省社会科学规划项目(14CGLJ37)
关键词
冷链品
多温共配
定时配送
车辆路径问题
随机需求
perishable goods
multi-temperature ioint distribution
regular distribution
vehicle routing problem
stochastic demand
