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【背景】全球低碳运输转型加速推进，多式联运系统面临需求模糊性与多目标协同优化的双重挑战。【目标】需求不确定条件下实现低碳多式联运路径优化，协同提升环境效益与运营效率。【方法】融合模糊期望值理论与NSGA-Ⅲ算法，构建考虑碳排放、成本、效率与客户满意度的多目标优化模型。【数据】设置20节点贸易网络虚拟案例，根据货运量和品类划分运输方式优先级。【结果】碳排放降低16.9%，物流成本减少12.0%，运输时效提升24.7%，同时客户对模糊时间窗口与货物完整性的满意度分别提升32.8%和13.6%。【结论】算法通过智能资源分配与路径规划，验证了需求不确定条件下经济-环境-服务三维目标协同优化的可行性。【应用】为交通管理部门提供兼顾低碳目标与运营弹性的决策支持工具，助力构建可持续多式联运网络。
Abstract：[Background] With the accelerated transformation of global low-carbon transportation,multimodal transportation systems are facing the dual challenges of demand ambiguity and multi-objective collaborative optimization. [Objective] Realize a low-carbon multimodal transport-route optimization model under uncertain demand conditions to achieve synergistic improvement of environmental benefits and operational efficiency. [Method] The fuzzy expectation theory and NSGA-Ⅲ algorithm were integrated to construct a multi-objective optimization model, considering carbon emissions, cost, efficiency, and customer satisfaction. [Data] A simulated 20-node trade network case was established, prioritizing transport modes based on freight volumes and commodity categories. [Results] In a virtual case of the 20-node trade network, carbon emissions were reduced by 16.9%; logistics costs were reduced by 12.0%; transportation timeliness was improved by 24.7%; and customer satisfaction values with the fuzzy time window and cargo integrity were enhanced by 32.8% and13.6%, respectively. [Conclusion] Through intelligent resource allocation and path planning, the proposed algorithm verified the feasibility of collaborative optimization of three-dimensional(economicenvironmental-service) objectives under uncertain demand conditions. [Application] This study equips transportation authorities with decision-support tools that consider low-carbon goals and operational resilience, and helps build a sustainable multimodal transport network.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.05.035
中图分类号:X322;F512.4
引用信息:
[1]王宇婷,王迪.模糊需求驱动的低碳多式联运路径优化[J].交通运输工程与信息学报,2025,23(04):196-206.DOI:10.19961/j.cnki.1672-4747.2025.05.035.
基金信息:
山东省自然科学基金项目(ZR2024QG174); 山东省城市更新学会重点研究专项项目(SURS240603)