轴承柔性智能制造的物料配送期量动态优化方法  

A Dynamic Optimization Method of Material Distribution Intervals and Quantity for Bearing Flexible Intelligent Manufacturing

作　　者：李红岩 杨晓英[1,2] 赵恒喆 张志文[1,2] LI Hongyan;YANG Xiaoying;ZHAO Hengzhe;ZHANG Zhiwen(School of Mechanical Engineering,Henan University of Science and Technology,Luoyang 471003,China;Collaborative Innovation Center of Advanced Manufacturing Machinery of Henan Province,Henan University of Science and Technology,Luoyang 471003,China)

机构地区：[1]河南科技大学机电工程学院,河南洛阳471003 [2]河南科技大学机械装备先进制造河南省协同创新中心,河南洛阳471003

出　　处：《工业工程》2023年第2期155-162,共8页Industrial Engineering Journal

基　　金：山东省重点研发计划资助项目(2020CXGC011001)。

摘　　要：针对传统物料配送方式难以满足轴承柔性智能生产的问题,提出自适应多品种变批量的物料配送期量优化方法。首先,以配送成本、线边库存成本、自动导引运输车数量为优化目标,考虑物料数量、自动导引运输车配送能力、配送时间等约束,构建多目标协同的多频次少批量物料配送期量优化模型;然后,根据模型决策变量的特征,采用反映配送信息的实值进行编码,设计改进拥挤度计算方法和改进精英策略的快速非支配排序遗传算法,提高算法寻优能力;最后,通过实例应用对优化方法进行验证。研究结果表明,较优化前,平均配送批量减少42%,平均配送间隔期缩短30%,总配送成本可减少17%以上,实现了不同型号下物料配送期量的自适应与自决策,有效降低了配送总成本。Aiming at the problem that traditional material distribution methods are difficult to meet the requirements of flexible intelligent manufacturing of bearings,an adaptive optimization method of material distribution intervals and quantity with multiple varieties and variable batch size is proposed.First,taking the distribution cost,the cost of inventory beside a production line and the number of automated guided vehicles as the optimization objectives,and taking the material quantity,the distribution capacity of automated guided vehicles and the distribution time as constraints,a multi-objective cooperative optimization model for distribution intervals and quantity of multi-frequency and small-batch materials is established.Then,according to the characteristics of the decision variables in the optimization model,using real values that reflect distribution information for coding,a fast non-dominated sorting genetic algorithm is designed with modified crowding calculation method and elitism strategy to improve the optimization ability of genetic algorithm.Finally,the proposed optimization method is verified with an application example.Results show that:compared to non-optimized scenarios,the average distribution batch size is reduced by 42%,the average distribution interval is shortened by 30%,and the total distribution cost can be reduced by more than 17% after optimization,which realizes the self-adaptation and selfdecision of material distribution intervals and quantity under different bearing types,and effectively reduces the total distribution cost.

关 键 词：轴承 多目标 物料配送 自动导引运输车 遗传算法 

分 类 号：TH187[机械工程—机械制造及自动化]