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和服务质量。在实际应用中，时间窗约束成为物流配送优化的核心问题之一，因为它不仅涉及客户满意度的提升，还关系到资源的有效利用和环境影响的降低。本研究旨在针对物流配送中的时间窗约束问题提出一种高效的优化方法，以实现路径规划的精确性和经济性。为此，研究结合了改进的遗传算法与动态规划技术，通过引入自适应交叉变异机制和局部搜索策略，显著提升了算法的收敛速度和解的质量。同时，模型考虑了多种现实因素，如交通状况、车辆载重限制以及客户需求的动态变化，从而增强了其实用性和鲁棒性。实验结果表明，该方法能够在大规模实例中有效减少配送时间和总成本，相较于传统算法平均性能提升超过15%。此外，研究还开发了一种基于时间窗松弛度的优先级评估机制，为复杂场景下的决策提供了重要支持。总体而言，本研究的主要贡献在于提出了一种兼顾理论深度与实践需求的优化框架，为物流配送领域的时间窗约束问题提供了新的解决思路，并为未来相关研究奠定了坚实基础。
关键词：物流配送优化；时间窗约束；改进遗传算法；动态规划；路径规划
Abstract
Logistics distribution, as a critical component of modern supply chain management, directly affects the operational costs and service quality of enterprises. In practical applications, time-window constraints have become one of the core issues in logistics distribution optimization, as they not only enhance customer satisfaction but also relate to the effective use of resources and the reduction of environmental impacts. This study aims to propose an efficient optimization method for addressing time-window constraints in logistics distribution, achieving both the accuracy and cost-effectiveness of route planning. To this end, the research integrates an improved genetic algorithm with dynamic programming techniques, introducing adaptive crossover and mutation mechanisms as well as local search strategies, which significantly enhance the convergence speed and solution quality of the algorithm. Meanwhile, the model incorporates various real-world factors, such as traffic conditions, vehicle load limitations, and dynamic changes in customer demands, thereby strengthening its practicality and robustness. Experimental results demonstrate that this method effectively reduces delivery time and total costs in large-scale instances, achieving more than a 15% improvement in average performance compared to traditional algorithms. Additionally, the study develops a priority evaluation mechanism based on time-window relaxation, providing crucial support for decision-making in complex scenarios. Overall, the primary contribution of this research lies in proposing an optimization fr amework that balances theoretical depth with practical requirements, offering new insights into solving time-window constraint problems in logistics distribution and laying a solid foundation for future related studies.
Keywords: Logistics Distribution Optimization; Time Window Constraint; Improved Genetic Algorithm; Dynamic Programming; Path Planning
目 录
1绪论 1
1.1物流配送时间窗约束的研究背景 1
1.2时间窗约束优化的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2时间窗约束优化的理论基础 2
2.1物流配送中的时间窗定义 2
2.2时间窗约束优化的核心问题 3
2.3相关数学模型与算法概述 3
2.4时间窗约束对配送效率的影响 4
2.5理论框架在实际场景中的应用 4
3时间窗约束优化的关键技术分析 5
3.1基于动态规划的时间窗优化方法 5
3.2启发式算法在时间窗优化中的应用 5
3.3混合整数规划在时间窗约束中的作用 6
3.4数据驱动的时间窗优化策略研究 6
3.5技术实现中的难点与解决方案 7
4时间窗约束优化的实际案例研究 7
4.1实际物流配送场景的特征分析 7
4.2时间窗约束优化的案例设计与实施 8
4.3不同算法在案例中的对比分析 8
4.4时间窗优化对成本和效率的影响评估 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
