基于改进CNN的供应链物流路径规划算法  

Improved CNN Based Supply Chain Logistics Path Planning Algorithm

作　　者：杜静[1] DU Jing(Electronic Information School,Jinzhong Vocational&Technical College,Jinzhong Shaanxi 030600,China)

机构地区：[1]晋中职业技术学院电子信息学院,山西晋中030600

出　　处：《信息与电脑》2022年第12期38-40,共3页Information & Computer

摘　　要：为解决当下物流运输企业供应链物流运输配送路径规划不合理、物流运输配送的效率低下、无法根据不同客户的需求提供对应的配送方式与服务等制约物流运输行业发展的问题,结合改进卷积神经网络(Convolutional Neural Networks,CNN)结构参数,提出了一种全新的供应链物流路径规划算法设计。该方法通过建立基于时间窗的物流路径优化模型,改进CNN的路径规划算法,设计了一种规划算法。实验测试结果证明,新的供应链物流路径规划算法运输路程与运输成本均低于传统的路径规划算法,对提高物流行业的可持续发展具有促进作用。In order to solve the problem that the current supply chain logistics and transportation route planning of logistics and transportation enterprises is unreasonable, which reduces the efficiency of logistics and transportation, and can’t provide corresponding distribution methods and services according to the needs of different customers, thus restricting the development of logistics and transportation industry. Combined with the improvement of Convolutional Neural Networks(CNN) structure parameters, the algorithm design of supply chain logistics path planning is proposed. By establishing a logistics path optimization model based on time window and designing an improved CNN path planning algorithm, a brand-new planning algorithm is designed. Experiments show that the transportation distance and cost of the new supply chain logistics path planning algorithm are lower than those of the traditional path planning algorithm, which can promote the sustainable development of the logistics industry.

关 键 词：供应链 物流运输 卷积神经网络(CNN) 时间窗 路径规划 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]