基于改进蚁群的供应链自动调度算法研究  

Research on supply chain automatic scheduling algorithm based on improved ant colony

作　　者：马仲能 李春晖 周松涛 赖莉敏 梁远星 MA Zhongneng;LI Chunhui;ZHOU Songtao;LAI Limin;LIANG Yuanxing(Guangzhou Power Supply Bureau,Guangdong Power Grid Co.,Ltd.,Guangzhou 510700,China)

机构地区：[1]广东电网有限责任公司广州供电局,广东广州510700

出　　处：《电子设计工程》2025年第6期44-47,52,共5页Electronic Design Engineering

基　　金：2022年广东电网有限责任公司广州供电局科技项目(GZHKJXM20210028)。

摘　　要：针对电力供应链中物资资源调度流程复杂、调度时间尺度长等问题,文中设计了一种以几何法为基础的改进蚁群调度优化算法。该算法在传统蚁群算法的基础上,引入几何法对初始信息素进行差异化设计,加快了算法的收敛速度,并融入以起始点和目标点为导向的启发式函数,避免了蚁群算法在寻优迭代过程中的锁死问题,改进后信息素的更新方式提高了算法在供应链自动调度过程中的收敛速度。在Matlab中进行的仿真实验结果表明,改进蚁群算法在4种仿真环境下的路径长度均在35以下,迭代次数约为35次,运行时间和长度方差均为8以下,提高了调度优化过程的收敛速度且降低了调度时间尺度。In response to the complex scheduling process and long scheduling time scale of material resources in the power supply chain,an improved ant colony scheduling optimization algorithm based on geometric methods is designed in this paper.On the basis of traditional ant colony algorithm,this algorithm introduces geometric method for differential design of initial pheromones,accelerating the convergence speed of the algorithm,and incorporates heuristic functions guided by starting and target points to avoid the locking problem in the optimization iteration process of ant colony algorithm.The improved pheromone update method improves the convergence speed of ant colony algorithm in the automatic supply chain scheduling process.The simulation experiments conducted in Matlab show that the improved ant colony algorithm has a path length of less than 35,an iteration number of about 35,and a running time and length variance of less than 8 in all four simulation environments.This improves the convergence speed of the scheduling optimization process and reduces the scheduling time scale.

关 键 词：改进蚁群算法 电力供应链 优化调度 几何法 改进信息素 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]