摘 要
物流配送作为现代供应链管理中的关键环节，其效率直接影响企业的运营成本与服务质量。随着电子商务的迅猛发展和消费者需求的日益多样化，物流配送面临路径规划复杂化、时效性要求提高等诸多挑战。为解决这一问题，本文聚焦于物流配送中的路径优化算法研究，旨在通过改进传统算法并引入新兴技术，提升路径规划的效率与精度。研究基于经典的旅行商问题（TSP）模型，结合实际物流场景中的动态约束条件，提出了一种融合遗传算法与模拟退火算法的混合优化方法。该方法通过遗传算法的全局搜索能力和模拟退火算法的局部优化能力相结合，有效解决了大规模配送网络中路径规划的复杂性问题。此外，本文还引入了实时交通数据和机器学习预测模型，以应对配送过程中的不确定性因素。实验结果表明，所提出的算法在路径长度、配送时间和计算效率等方面均优于单一的传统优化算法。具体而言，相较于经典遗传算法，新方法在测试案例中的路径总长度平均缩短了15%，计算时间减少了约20%。研究结论显示，混合优化算法能够显著提升物流配送的效率，并为实际应用提供了可行的技术支持。本文的主要贡献在于将多源数据与智能算法相结合，提出了适用于动态环境的路径优化解决方案，为未来物流配送系统的智能化升级奠定了理论基础。
关键词：物流配送；路径优化算法；遗传算法；模拟退火算法；实时交通数据
Abstract
Logistics distribution, as a critical component of modern supply chain management, directly affects the operational costs and service quality of enterprises. With the rapid development of e-commerce and the increasing diversification of consumer demands, logistics distribution faces numerous challenges, such as the complexity of route planning and heightened requirements for timeliness. To address these issues, this study focuses on the research of route optimization algorithms in logistics distribution, aiming to enhance the efficiency and accuracy of route planning by improving traditional algorithms and incorporating emerging technologies. Based on the classical Traveling Salesman Problem (TSP) model and integrating dynamic constraint conditions from real-world logistics scenarios, a hybrid optimization method that combines genetic algorithms with simulated annealing algorithms is proposed. This method effectively resolves the complexity of route planning in large-scale distribution networks by leveraging the global search capability of genetic algorithms and the local optimization capability of simulated annealing algorithms. Additionally, real-time traffic data and machine learning prediction models are introduced to handle uncertainties during the delivery process. Experimental results demonstrate that the proposed algorithm outperforms single traditional optimization algorithms in terms of route length, delivery time, and computational efficiency. Specifically, compared with the classical genetic algorithm, the new method reduces the average total route length by 15% and decreases computation time by approximately 20% in test cases. The conclusions indicate that the hybrid optimization algorithm significantly improves the efficiency of logistics distribution and provides feasible technical support for practical applications. The primary contribution of this study lies in integrating multi-source data with intelligent algorithms to propose a route optimization solution adaptable to dynamic environments, thereby laying a theoretical foundation for the intelligent upgrading of future logistics distribution systems.
Keywords: Logistics Distribution; Path Optimization Algorithm; Genetic Algorithm; Simulated Annealing Algorithm; Real-Time Traffic Data
目 录
1绪论 1
1.1物流配送路径优化的研究背景 1
1.2路径优化算法研究的意义与价值 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2物流配送路径优化问题分析 2
2.1物流配送的基本特征与挑战 2
2.2路径优化问题的数学建模 3
2.3影响路径优化的关键因素分析 3
2.4实际场景中的约束条件探讨 4
2.5路径优化目标的多维度解析 4
3常见路径优化算法及其应用 5
3.1启发式算法在物流配送中的应用 5
3.2元启发式算法的性能评估与改进 5
3.3精确算法在小规模问题中的适用性 6
3.4混合算法的设计思路与实现方式 6
3.5不同算法的对比分析与选择策略 7
4路径优化算法的实际案例研究 7
4.1案例背景与数据来源说明 7
4.2算法设计与参数调优过程 8
4.3实验结果分析与效果验证 8
4.4算法在动态环境下的适应性测试 9
4.5案例研究的总结与启示 9
结论 10
参考文献 11
致 谢 12
