基于数据支撑的机车检修智慧物流配送应用  

Intelligent logistics distribution application for locomotive maintenance based on data support

作　　者：王海伟 张德茂 王刚 王奕标 Wang Haiwei;Zhang Demao;Wang Gang;Wang Yibiao

机构地区：[1]国能铁路装备有限责任公司沧州机车车辆维修分公司 [2]北京华科众合科技有限公司

出　　处：《现代科学仪器》2023年第5期163-168,共6页Modern Scientific Instruments

摘　　要：针对铁路装备运输管理混乱、物流成本高的问题,研究构建了配件供给的节点-路线联合优化模型,并利用遗传算法求解最小成本目标函数。同时研究构建了关于路径规划下的运输成本目标函数,形成多目标优化模型。实验结果显示,GA-NRM模型的训练结果的最下成本比NRM模型的最小成本少175元,同时将遗传算法和粒子群算法进行成本控制比较,发现GA算法下最小成本比PSO算法低1.97万元。实验证明了遗传算法求解NRM目标函数的有效性,同时也表明GA-NRM模型能够处理多种变量和总成本之间的关系,为铁路装备企业提供优化的配件供应网络规划策略。Aiming at the problems of chaotic transportation management and high logistics cost of railway equipment,a node-route joint optimization model for accessories supply is studied and constructed,and genetic algorithm is used to solve the minimum cost objective function.At the same time,the transportation cost objective function for path planning is constructed to form a multi-objective optimization model.The experimental results show that the minimum cost under GA-NRM model is 175 yuan less than that under NRM model.At the same time,the cost control under genetic algorithm and particle swarm optimization algorithm is compared.It is found that the minimum cost under GA algorithm is 19700 yuan lower than that under PSO algorithm.The experiment proves the effectiveness of the genetic algorithm to solve the NRM objective function,and also shows that the GA-NRM model can deal with the relationship between multiple variables and total cost,which provides the railway equipment enterprises with optimized spare parts supply network planning strategy.

关 键 词：配件供给 节点-路线联合优化 遗传算法 GA-NRM模型 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]