不确定需求下面向货物时间价值的多式联运智能路径规划

Intelligent Path Planning for Intermodal Transport Considering Time Value of Goods under Uncertain Demand

摘要: 随着综合交通运输体系的持续完善，多式联运凭借降低物流成本、提升运输效率及促进绿色低碳发展的综合优势，已成为现代物流组织的重要发展方向。然而，物流市场需求呈现多样化、动态化与不确定化特征，传统以单一成本或时间为主的路径优化方法已难以全面反映现实决策需求。货物在运输过程中普遍具有时间价值属性，不同类型货物在价值水平、衰减速度和敏感程度方面存在差异，运输时间延长不仅带来直接损耗，还可能引起货物价值降低、库存占压增加和市场响应能力下降。基于此，本文在需求不确定背景下，将货物时间价值理论引入多式联运路径优化问题，构建以运输成本、货物时间价值成本和碳排放成本综合最小为目标的路径优化模型。针对因季节、供应链管理等导致的需求量不确定问题，采用情景分析与鲁棒优化方法刻画需求波动，建立不确定需求下的鲁棒路径优化模型。在此基础上，设计并开发多式联运智能路径规划平台，实现货物时间价值分类、需求情景设置、双目标路径优化和结果可视化等功能。通过设计相应的算例验证，所构建模型能够在需求波动条件下有效兼顾经济性、时效性和稳定性，所设计平台具备较好的决策支持能力与实际应用价值。

Abstract: As the comprehensive transportation system continues to improve, intermodal transport has become an important direction for modern logistics organization, owing to its comprehensive advantages of reducing logistics costs, improving transport efficiency, and promoting green and low-carbon development. However, logistics market demand is characterized by diversification, dynamics, and uncertainty. Traditional path optimization methods that focus mainly on a single objective such as cost or time can no longer fully reflect real-world decision-making needs. Goods generally have time value attributes during transportation, and different types of goods differ in value level, decay rate, and sensitivity. Extended transport time not only leads to direct losses but may also cause a decrease in goods value, increased inventory holding costs, and reduced market responsiveness. Given this, under demand uncertainty, this paper introduces the theory of time value of goods into the intermodal transport path optimization problem. A path optimization model is constructed with the objective of minimizing the sum of transport cost, time value cost of goods, and carbon emission cost. To address the demand uncertainty caused by factors such as seasonality and supply chain management, scenario analysis and robust optimization methods are used to characterize demand fluctuations, and a robust path optimization model under uncertain demand is established. Based on this, an intelligent intermodal transport path planning platform is designed and developed, incorporating functions such as classification of goods time value, demand scenario setting, bi-objective path optimization, and result visualization. Numerical experiments demonstrate that the proposed model effectively balances economy, timeliness, and stability under demand fluctuations, and the designed platform exhibits strong decision support capability and practical application value.