用强化学习动态拒单与规划路线，同时控制碳排放总量。
Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota
- 分两层优化：先预判拒单，再生成新路线
- 在不确定时间窗口下仍显著降低碳排放
- 适合需要环保合规的即时配送场景
本文提出并形式化了动态随机车辆路径问题带碳排放配额（DS-QVRP-RR），这是一个融合动态需求接受与路径规划、且受全局碳排放约束的新问题。核心贡献是一个双层优化框架，用于实现需求的前瞻式拒绝和新路线生成。为求解该问题，我们开发了将强化学习与组合优化相结合的混合算法。通过全面的计算实验，对比了该方法与传统方法的表现。结果表明，无论输入类型如何，即使在问题时间范围不确定的情况下，本方法仍具显著有效性。
原文摘要 · Abstract (English)
This paper introduces and formalizes the Dynamic and Stochastic Vehicle Routing Problem with Emission Quota (DS-QVRP-RR), a novel routing problems that integrates dynamic demand acceptance and routing with a global emission constraint. A key contribution is a two-layer optimization framework designed to facilitate anticipatory rejections of demands and generation of new routes. To solve this, we develop hybrid algorithms that combine reinforcement learning with combinatorial optimization techniques. We present a comprehensive computational study that compares our approach against traditional methods. Our findings demonstrate the relevance of our approach for different types of inputs, even when the horizon of the problem is uncertain.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。