基于隐马尔可夫模型的智能物流路径优化  

Intelligent Logistics Path Optimization Based on Hidden Markov Model

作　　者：张静 程艳 ZHANG Jing;CHENG Yan(Jinzhong College of Information,Taigu 030800,China)

机构地区：[1]晋中信息学院,山西太谷030800

出　　处：《物流科技》2025年第6期5-8,共4页Logistics Sci Tech

基　　金：山西省教育科学“十四五”规划课题“OBE理念下概率论与数理统计课程教学体系的构建与实践研究”阶段性研究成果(GH-220236)。

摘　　要：传统的路径规划方法大多依赖静态数据或较为简单的算法,在面对实际物流过程中复杂多变的环境因素时显得力不从心。文章旨在深入探讨隐马尔可夫模型在智能物流路径优化领域的运用。首先,全面剖析了隐马尔可夫模型的基本原理,并详细阐述了其在物流路径优化中的理论支撑;其次,阐述了模型构建、参数估计、最优预测、动态调整机制等关键部分;最后,利用SimPy仿真平台对所提方法进行了构建与测试,并将其与传统A*算法和Dijkstra算法进行了对比分析。结果表明,文章所提方法可以缩短路径长度,降低平均运行时间,研究深入探讨了隐马尔可夫模型(HMM)在动态路径优化领域的应用潜力,为智能物流路径优化问题提供了坚实的理论支撑与有效的实践指导。Traditional path planning methods often rely on static data or simple algorithms,making it challenging to adapt to changing environmental factors in real logistics processes.This paper aimed to thoroughly explore the application of the Hidden Markov Model(HMM)in intelligent logistics path optimization.First,it systematically analyzed the fundamental principles of HMM and its theoretical basis for logistics path optimization.Then,it elaborated on key components such as model construction,parameter estimation,optimal prediction,and dynamic adjustment mechanisms.Finally,the method was developed and testedby using the SimPy simulation platform,with comparative analysis against traditional A*and Dijkstra algorithms.The results indicated that this method could effectively shorten path length and reduce average runtime.This study demonstrated the potential of HMM in dynamic path optimization,aiming at providing theoretical support and practical guidance for addressing intelligent logistics path optimization challenges.

关 键 词：智能物流 路径规划 隐马尔可夫模型 不确定性 

分 类 号：F259.2[经济管理—国民经济]