摘 要：随着现代物流行业的快速发展，路径规划问题成为提升物流效率和降低成本的关键环节。本研究以优化理论为基础，针对物流路径规划中的复杂约束条件和动态变化特征，提出了一种结合遗传算法与模拟退火算法的混合优化方法。该方法通过改进传统算法的搜索策略，显著提高了求解效率和全局最优性。研究选取实际物流配送场景进行仿真分析，结果表明，所提方法能够在较短时间内生成更优的配送路径方案，有效减少运输距离和时间成本。此外，研究还引入了实时交通数据和动态调整机制，进一步增强了模型在实际应用中的适应性和鲁棒性。本研究的主要创新点在于将多目标优化与动态环境相结合，为复杂物流路径规划提供了新的解决思路，其研究成果对推动物流行业智能化发展具有重要意义。
关键词：物流路径规划；混合优化方法；遗传算法；模拟退火算法；动态调整机制
Application of Optimization Theory in Logistics Path Planning
英文人名
Directive teacher:×××
Abstract：With the rapid development of the modern logistics industry, path planning has become a critical factor in enhancing logistics efficiency and reducing costs. Based on optimization theory, this study proposes a hybrid optimization method that integrates genetic algorithms with simulated annealing algorithms to address the complex constraints and dynamic characteristics inherent in logistics path planning. By improving the search strategy of traditional algorithms, the proposed method significantly enhances solution efficiency and global optimality. The research conducts simulation analyses using real-world logistics distribution scenarios, demonstrating that the method can generate superior delivery route plans within a shorter time fr ame, effectively reducing transportation distance and time costs. Additionally, the study incorporates real-time traffic data and a dynamic adjustment mechanism, further strengthening the model's adaptability and robustness in practical applications. A major innovation of this study lies in combining multi-ob jective optimization with dynamic environments, offering a novel approach to solving complex logistics path planning problems. The findings contribute significantly to advancing the intelligent development of the logistics industry.
Keywords: Logistics Path Planning；Hybrid Optimization Method；Genetic Algorithm；Simulated Annealing Algorithm；Dynamic Adjustment Mechanism
目 录
引言 1
一、物流路径规划的优化理论基础 1
(一)优化理论的基本概念 1
(二)物流路径规划的核心问题 2
(三)优化方法在物流中的适用性 2
二、经典优化模型在物流路径中的应用 3
(一)最短路径模型的应用分析 3
(二)车辆路径问题的建模方法 3
(三)动态规划在路径优化中的实践 4
三、现代优化算法对物流路径的改进 4
(一)遗传算法在路径规划中的实现 4
(二)模拟退火算法的优化效果评估 5
(三)粒子群算法的应用与局限性 5
四、实际案例与优化理论的结合研究 5
(一)城市配送路径的优化实践 6
(二)多目标路径规划的案例分析 6
(三)数据驱动的路径优化策略 6
结论 7
参考文献 8
致谢 8
