基于拉格朗日松弛算法的冷链商品物流配送优化方法  

Optimization Method of Cold Chain Commodity Logistics Distribution Based on Lagrange Relaxation Algorithm

作　　者：陈文[1] CHEN Wen(Fujian Boat Transportation Vocational College,Fuzhou 350007,China)

机构地区：[1]福建船政交通职业学院,福州350007

出　　处：《长春工程学院学报（自然科学版）》2022年第2期124-128,共5页Journal of Changchun Institute of Technology：Natural Sciences Edition

摘　　要：网商平台的快速发展,对冷链产品的配送效率提出了更高的要求,而冷链产品配送受运输车辆、交通路况以及天气状态等因素的制约较多,针对多回路、多配送点位、多供货点的冷链产品的配送最佳路径求解的业界难题,将启发式搜索算法的应用与拉格朗日松弛算法相结合,用于冷链产品的复杂路径求解,结合综合因素对拉格朗日松弛算法进行动态自适应的调整,对冷链配送的网络模型进行动态优化调整,获取最优路径解。仿真对比实验显示,该算法对比原始的粒子群优化算法,获得最佳路径的时间更短、最佳路径的求解能力更高。The rapid development of e-commerce platform puts forward higher requirements for the distribution efficiency of cold chain products.However,the distribution of cold chain products is more restricted by transport vehicles,traffic conditions,weather conditions and other factors.It is an industry problem to solve the best distribution path of cold chain products with multiple loops,multiple distribution points and multiple supply points.In this paperthe heuristic search algorithm and Lagrange relaxation algorithm are used to solve the complex path problem of cold chain products.Combined with comprehensive factors,the Lagrange relaxation algorithm is adjusted dynamically and adaptively,and the network model of cold chain distribution is adjusted dynamically to obtain the optimal path solution.The simulation results show that the proposed algorithm of this paper has shorter time to get the best path and higher solving ability compared with original particle swarm optimization algorithm.

关 键 词：大数据搜索 数据挖掘 特征项 结果集排序 

分 类 号：O221.4[理学—运筹学与控制论]