基于改进遗传算法对LIRP问题集成优化研究

Research on integrated optimization of LIRP problem based on improved genetic algorithm

在物流系统优化中,物流节点的选址、库存控制及运输路径优化是进行物流系统规划最重要的模块,三个方面相互关联相互制约,在企业物流系统运作中缺一不可,也是当今企业降低成本提高利润的大势所趋.就选址、库存、车辆路径三个要素进行探讨,构建在需求不确定情形下的LIRP集成优化模型,解决分销中心的选址、库存、分销中心到需求门店的路径优化、需求门店的库存优化问题,充分将物流配送选址、库存控制、车辆路径三个方面进行有效集成,采用改进的遗传算法对模型的求解,并以连锁食品R公司为算例,编写程序代码,借助Matlab计算工具求解,最后将求解的结果进行优化对比分析,节约了R公司总里程3964 km,车辆减少36辆,总物流成本减少2.85万元,并验证了算法的有效性.

In logistics system optimization,the selection of logistics nodes,inventory control,and transportation path optimization are the most important modules for logistics system planning.These three levels are interrelated and mutually restrictive,and are indispensable in the operation of enterprise logistics systems.They were also the current trend for enterprises to reduce costs and improve profits.This paper explored the three elements of location selection,inventory,and vehicle routing,constructed a LIRP integrated optimization model under uncertain demand,solved the problems of distribution center location selection,inventory,distribution center to demand store path optimization,and demand store inventory optimization,effectively integrated logistics distribution location selection,inventory control,and vehicle routing,used an improved genetic algorithm to solve the model,and took chain food R company as an example to write program code,used Matlab calculation tools to solve it.Finally,the optimization results were compared and analyzed,saving R Company a total mileage reduction of 3964 km,a reduction of 36 vehicles,and a total logistics cost reduction of 28500 yuan.The algorithm was verified at the same time.