摘要
对农产品冷链物流的碳排放量多、货损率高的问题,考虑碳排放成本、货损成本和客户时间窗等成本后,构建了考虑碳排放的农产品冷链物流的配送车辆路径优化模型;用邻域搜索对人工蜂群算法改进后用于模型求解;以北京华康绿源食品有限公司的26个顾客作为配送点,根据每个配送点的地理位置坐标、需求量、服务时间和时间窗等数据分别采用基本的人工蜂群算法和改进的人工蜂群算法进行求解,实验结果证明改进后的人工蜂群算法有效降低了农产品冷链物流成本。
To solve the problem of high carbon emissions and high damage rate of agricultural products cold chain logistics, considering the cost of carbon emission, cargo damage cost and customer time window, a distribution vehicle routing optimization model is constructed. The neighborhood search is used to improve the artificial bee colony algorithm and to solve the model;26 customers of Beijing Huakang Luyuan Food Co., Ltd. are used as distribution points, according to the geographical coordinates, demand quantity, service time and time window of each distribution point. The data is solved by the basic artificial bee colony algorithm and the improved artificial bee colony algorithm. The experimental results show that the improved artificial bee colony algorithm can effectively reduce the cost of agricultural cold chain logistics.
作者
段砚
蒋洪伟
DUAN Yan;JIANG Hongwei(School of Information Management,Beijing Information Science&Technology University,Beijing 100192)
出处
《北京信息科技大学学报（自然科学版）》
2019年第6期92-96,共5页
Journal of Beijing Information Science and Technology University
关键词
农产品冷链物流
碳排放
配送车辆路径
人工蜂群算法
cold chain logistics of agricultural products
carbon emission
distribution vehicle routing
artificial bee colony algorithms
