Zou Yuansu, Gao Qixian, Wu Hao, Liu Nianbo
University of Electronic Science and Technology of China, Chengdu 611731, China.
Xihua University, Chengdu 610039, China.
Sensors (Basel). 2024 Nov 22;24(23):7461. doi: 10.3390/s24237461.
Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route of fresh products, considering factors such as carbon emissions, time windows, and cooling costs. By calculating carbon emission costs through carbon taxes, the model aims to minimize distribution costs. With a graph attention network structure adopted to describe node locations, accessible paths, and data with collection windows for path planning, it integrates to solve for the optimal distribution routes, taking into account carbon emissions and cooling costs under varying temperatures. Extensive simulation experiments and comparative analyses demonstrate that the proposed time-window-constrained reinforcement learning model provides effective decision-making information for optimizing fresh product fresh food supply chain transportation and distribution, controlling logistics costs, and reducing carbon emissions.
智能交通系统（ITSs）利用物联网（IoT）技术促进车辆、基础设施和用户之间的智能互联，从而优化交通流量。本文构建了一个生鲜产品新鲜度供应链配送路线优化模型，考虑了碳排放、时间窗和冷藏成本等因素。通过碳税计算碳排放成本，该模型旨在使配送成本最小化。采用图注意力网络结构来描述节点位置、可达路径以及带有收集窗口的数据用于路径规划，它综合考虑不同温度下的碳排放和冷藏成本来求解最优配送路线。大量的仿真实验和对比分析表明，所提出的时间窗约束强化学习模型为优化生鲜产品新鲜度供应链运输与配送、控制物流成本以及减少碳排放提供了有效的决策信息。
