发表机构
Centre INRIA de l’Université de Lille; Ecole Polytechnique Fédérale de Lausanne（里尔大学INRIA中心; 洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出动态电动自动驾驶按需出行问题，采用组合优化增强机器学习策略，联合优化请求选择、路线与充电，在320个实例上平均6秒求解近500请求，目标值降低8.7%-14.3%。
AI 中文摘要
本研究提出了一种基于决策时段的动态电动自动驾驶按需出行问题（Dyn-EADARP），其中新到达的请求在周期性决策时段被收集和处理。一个关键决策不仅是如何服务请求，还包括何时派发它们。在每个决策时段，服务提供者决定服务哪些请求以及推迟哪些请求，同时联合确定车辆路线、时间表和充电决策。现有计划中未执行的部分可随着新信息的获取而进行修订。为解决此问题，我们遵循组合优化增强机器学习（COAML）框架开发了一种ML-CO策略，该框架将统计模型与组合优化层相结合以进行决策。统计模型为可用请求预测奖励值，而一个带奖励收集的E-ADARP利用这些奖励值来联合确定请求选择、路线规划、时间安排和充电计划。统计模型经过直接训练，以改进由优化层产生的决策。在320个测试实例上的计算实验证明了ML-CO的效率，它平均在约6秒内解决了包含近500个请求的实例。与基准策略相比，其目标值降低了8.7%至14.3%，与预期参考值的平均差距为4.8%。结果提供了若干管理见解。首先，立即服务请求并非总是最优的，因为选择性地推迟某些请求可以创造更好的拼车机会。其次，修订现有计划保持了运营灵活性，并显著提高了解决方案质量。最后，更频繁的决策并不一定改善性能，这凸显了选择适当决策频率的重要性。
英文摘要
This study introduces a decision-epoch-based dynamic electric autonomous dial-a-ride problem (Dyn-EADARP), in which incoming requests are collected and processed at periodic decision epochs. A key decision is not only how to serve requests, but also when to dispatch them. At each decision epoch, the service provider decides which requests to serve and which to postpone, while jointly determining vehicle routes, schedules, and charging decisions. Unexecuted parts of existing plans can be revised as new information becomes available. To solve this problem, we develop an ML--CO policy following the combinatorial optimization augmented machine learning (COAML) framework, which combines a statistical model with a combinatorial optimization layer for decision making. The statistical model predicts prizes for available requests, and a prize-collecting E-ADARP uses these prizes to jointly determine request selection, routing, scheduling, and charging. The statistical model is trained directly to improve the decisions produced by the optimization layer. Computational experiments on 320 test instances demonstrate the efficiency of ML--CO, which solves instances with nearly 500 requests in about 6 seconds on average. It achieves 8.7%--14.3% lower objective values than benchmark policies and an average gap of 4.8% to the anticipative reference. The results provide several managerial insights. First, serving requests immediately is not always best, as selectively postponing some requests can create better ride-sharing opportunities. Second, revising existing plans preserves operational flexibility and substantially improves solution quality. Finally, more frequent decision making does not necessarily improve performance, highlighting the importance of choosing an appropriate decision frequency.
