摘要
为提高烟草物流配送服务水平、降低配送成本,通过引入工作量均衡指标,建立了低碳背景下的碳排放数学模型,并提出了改进的双层遗传算法用于优化物流路径。第一层遗传算法以工作量均衡为目标进行聚类,将多车辆多服务点问题转换为单车辆多服务点配送;第二层设计了禁忌遗传算法,通过增加禁忌表的记忆功能,提高模型求解的精确性。以浙江烟草商业物流配送中心为对象,对本文算法与模型的实际效果进行验证,结果表明:本文算法在计算时间和收敛效果上均优于单层遗传算法;物流路径优化后配送成本降低25.4%,批零效率提高36.8%,中转效率提高33.9%。该算法和模型为优化烟草物流配送路径提供了技术支持。
In order to promote distribution service and reduce delivery cost of tobacco logistics, a carbon emission mathematical model on a background of low carbon was established by introducing workload equilibrium index, and an improved double-layer genetic algorithm was proposed to optimize delivery route. Taking workload balancing as a target and by way of clustering, the first layer converted an issue of multi-vehicle to multi-service point into an issue of single-vehicle to multi-service point. In the second layer, a tabu genetic algorithm was designed to promote the precision of model through adding a memory function to the tabu list. Taking the distribution center of Zhejiang Provincial Tobacco Corporation as an object, the said algorithm and the model were validated. The results showed that the algorithm was superior to single-layer genetic algorithm in terms of calculation time and convergence effect. The optimized delivery route decreased the delivery cost by 25.4%, increased average wholesale efficiency by 36.8%, and raised average transfer efficiency by 33.9%. The algorithm and the model provide technical supports for the optimization of delivery route in tobacco logistics.
出处
《烟草科技》
EI
CAS
CSCD
北大核心
2018年第1期85-92,共8页
Tobacco Science & Technology
关键词
烟草物流
配送成本
路径优化
双层遗传算法
低碳
禁忌遗传算法
Tobacco logistics
Delivery cost
Route optimization
Double-layer genetic algorithm
Low carbon
Tabu genetic algorithm
