发表机构
Adelaide University（阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对不确定性下的电动自动驾驶拨号乘车问题，研究双层大邻域搜索中的插入算子选择，比较六种方法，发现无单一最优策略，性能依实例而异。
AI 中文摘要
电动自动驾驶拨号乘车问题（EADARP）通过为电动车辆引入电池和充电约束，扩展了经典的拨号乘车问题。在实践中，出行时间的不确定性可能导致时间窗约束被违反。大邻域搜索在求解EADARP方面是有效的，但其性能可能依赖于修复阶段中插入算子的选择。本文研究了在确定性EADARP和机会约束EADARP变体的双层大邻域搜索框架内的插入算子选择。在机会约束变体中，弧段出行时间被建模为独立的服从正态分布的随机变量，并且上时间窗约束以概率方式强制执行。我们考虑了六种选择方法，即固定贪婪插入、固定遗憾值插入、随机选择、确定性基于状态的规则、性能自适应的ALNS选择以及基于LLM的状态感知选择。实验结果表明，在较小实例上性能相当，而在较大且约束更强的实例上差异变得更加明显。没有一种策略在所有实例上表现最佳，基于LLM、基于规则和ALNS策略的相对性能随问题实例和实验设置而变化。
英文摘要
The electric autonomous dial-a-ride problem (EADARP) extends the classical dial-a-ride problem by incorporating battery and charging constraints for electric vehicles. In practice, travel-time uncertainty can cause violations of time-window constraints. Large neighborhood search is effective for solving the EADARP, but its performance can depend on the choice of insertion operator during the repair phase. This paper investigates insertion-operator selection within a bilevel large neighborhood search framework for deterministic and chance-constrained variants of the EADARP. In the chance-constrained variant, arc travel times are modeled as independent normally distributed random variables, and upper time-window constraints are enforced probabilistically. We consider six selection methods, namely fixed greedy insertion, fixed regret-based insertion, random selection, a deterministic state-based rule, performance-adaptive ALNS selection, and LLM-based state-aware selection. Experimental results show comparable performance on smaller instances, while differences become more evident on larger and more constrained instances. There is no single strategy that performs best across all instances, and the relative performance of the LLM-based, rule-based, and ALNS strategies varies with the problem instance and experimental setting.
