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新鲜食品供应链中的碳效率调度与时间窗口受限的深度强化学习模型
Yuansu Zou1,2, Qixian Gao1, Hao Wu1
1University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究优化了使用智能运输系统 (ITS) 和物联网 (IoT) 的新鲜食品分销路线,以最大限度地降低成本和碳排放. 强化学习模型有效地管理物流,考虑时间窗口和冷却需求.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 供应链管理 供应链管理
- 运营研究 运营研究
背景情况:
- 智能运输系统 (ITS) 集成物联网 (IoT) 来增强车辆,基础设施和用户连接,优化交通流.
- 新鲜食品供应链在分销方面面临挑战,包括时间敏感性,温度控制和环境影响.
- 尽量减少物流成本和碳排放对于可持续的新鲜食品分配至关重要.
研究的目的:
- 为新鲜食品供应链分销路线开发一个优化模型.
- 尽量减少总的分配成本,包括碳排放成本和冷却成本.
- 加强决策,以优化新鲜产品的运输和分销.
主要方法:
- 构建了一个优化模型,将碳税纳入排放成本,时间窗口和冷却费用.
- 利用图形注意网络来表示节点位置,路径和数据收集窗口用于路径规划.
- 集成了一个时间窗口受限制的强化学习模型,以解决最佳分发路径.
主要成果:
- 拟议的模型有效地优化了新鲜食品的分销路线.
- 在物流成本和碳排放方面显著降低.
- 提供有效的决策信息,用于在不同温度条件下的供应链管理.
结论:
- 时间窗口受限制的强化学习模型为优化新鲜食品分配提供了有效的解决方案.
- 该模型成功地平衡了成本降低,时间限制和环境可持续性.
- ITS和物联网技术对于提高新鲜食品物流的效率和环境性能至关重要.
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