摘 要
物流网络作为现代供应链管理的核心组成部分，其节点选址优化问题直接影响运营效率与成本控制。本文针对物流网络中节点选址的复杂性与多目标特性，结合实际需求与理论研究，提出了一种基于混合智能算法的选址优化模型。研究背景源于当前物流网络面临的动态变化、不确定性和多约束条件，传统方法难以满足高效选址的需求。为此，本文以降低总成本、提升服务质量和增强网络鲁棒性为目标，设计了融合遗传算法与模拟退火算法的混合优化策略，并引入模糊集合理论处理不确定性因素。通过构建多层次评价指标体系，模型能够综合考虑经济、环境和社会效益等多方面影响。实验结果表明，该方法在大规模实例测试中表现出优异的收敛速度和解质量，显著优于单一算法或经典启发式方法。此外，本文创新性地将实时数据驱动机制融入选址过程，提高了模型对动态环境的适应能力。主要贡献在于提出了适用于复杂物流网络的智能化选址方案，为实际决策提供了科学依据和技术支持，同时拓展了相关领域的理论框架与应用范围。研究成果可广泛应用于城市配送中心、区域分拨中心及国际物流枢纽的规划与设计，具有重要的学术价值和实践意义。
关键词：物流网络优化；混合智能算法；节点选址；模糊集合
Logistics networks, as a core component of modern supply chain management, play a crucial role in operational efficiency and cost control, with node location optimization being a key determinant. In response to the complexity and multi-ob jective nature of node location problems in logistics networks, this study proposes an optimization model based on a hybrid intelligent algorithm, integrating practical requirements and theoretical research. The research context arises from the dynamic changes, uncertainties, and multiple constraints faced by contemporary logistics networks, which traditional methods struggle to address effectively. To tackle these challenges, this study designs a hybrid optimization strategy that combines genetic algorithms with simulated annealing, aiming to minimize total costs, enhance service quality, and strengthen network robustness. Fuzzy set theory is introduced to handle uncertainty factors, while a multi-level evaluation index system is constructed to comprehensively consider economic, environmental, and social benefits. Experimental results demonstrate superior convergence speed and solution quality in large-scale instance tests compared to single algorithms or classical heuristic methods. Additionally, this study innovatively incorporates a real-time data-driven mechanism into the location process, improving the model's adaptability to dynamic environments. The primary contribution lies in proposing an intelligent location scheme suitable for complex logistics networks, providing scientific evidence and technical support for practical decision-making while extending the theoretical fr amework and application scope of related fields. The research findings can be widely applied to the planning and design of urban distribution centers, regional sorting centers, and international logistics hubs, holding significant academic value and practical implications.
Keywords: Logistics Network Optimization; Hybrid Intelligent Algorithm; Node Location Selection; Fuzzy Set
目 录
1绪论 1
1.1物流网络节点选址的研究背景 1
1.2节点选址优化的意义与价值 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节点选址优化的理论基础 2
2.1物流网络的基本概念与特征 2
2.2节点选址优化的核心理论框架 3
2.3数学建模在选址优化中的应用 3
2.4不确定性条件下的选址理论探讨 4
2.5理论基础对实际问题的指导意义 4
3节点选址优化的关键影响因素 5
3.1成本因素对选址决策的影响 5
3.2地理位置与交通条件的作用 5
3.3需求分布与市场覆盖分析 6
3.4政策法规对选址优化的约束 6
3.5可持续发展视角下的选址考量 7
4节点选址优化的模型与算法设计 7
4.1基于线性规划的选址模型构建 7
4.2智能优化算法在选址中的应用 8
4.3多目标优化模型的设计与实现 8
4.4动态环境下的选址模型改进 9
4.5模型验证与案例分析 9
结论 10
参考文献 11
致 谢 12
