Although unmanned vehicle fleets offer efficiency in transportation, logistics and inspection, their susceptibility to failures poses a significant challenge to mission continuity. We study the Multi-Depot Rural Postman Problem with Rechargeable and Reusable Vehicles (MD-RPP-RRV) with vehicle failures, where unmanned rechargeable vehicles placed at multiple depots with capacity constraints may fail while serving arc-based demands. To address unexpected vehicle breakdowns during operation, we propose a two-stage real-time rescheduling framework. First, a centralized auction quickly generates a feasible rescheduling solution; for this stage, we derive a theoretical additive bound that establishes an analytical guarantee on the worst-case rescheduling penalty. Second, a peer auction refines this baseline through a problem-specific magnetic field router for local schedule repair, utilizing parameters calibrated via sensitivity analysis to ensure controlled computational growth. We benchmark this approach against a simulated annealing metaheuristic to evaluate solution quality and execution speed. Experimental results on 257 diverse failure scenarios demonstrate that the framework achieves an average runtime reduction of over 95\% relative to the metaheuristic baseline, cutting rescheduling times from hours to seconds while maintaining high solution quality. The two-stage framework excels on large-scale instances, surpassing the centralized auction in nearly 80\% of scenarios with an average solution improvement exceeding 12\%. Moreover, it outperforms the simulated annealing mean and best results in 59\% and 28\% of scenarios, respectively, offering the robust speed-quality trade-off required for real-time mission continuity.

翻译：尽管无人车队在运输、物流和巡检中展现出高效性，但其易故障特性对任务连续性构成了重大挑战。本文研究了存在车辆故障的、配备可充电可重复使用车辆的多车场乡村邮路问题（MD-RPP-RRV），其中容量受限的多车场部署的可充电无人车辆在服务基于弧段的需求时可能发生故障。为应对运行中突发的车辆故障，我们提出了一种两阶段实时重调度框架。首先，集中式拍卖快速生成可行的重调度方案；针对此阶段，我们推导了一个理论加性界，为最坏情况下的重调度惩罚提供了分析性保证。其次，对等拍卖通过一个针对特定问题的磁场路由器进行局部调度修复，从而优化该基准方案；该阶段利用经灵敏度分析校准的参数，以确保计算量的可控增长。我们通过模拟退火元启发式算法对本方法进行基准测试，以评估解的质量和执行速度。在257个多样化故障场景上的实验结果表明，相对于元启发式基准，该框架实现了平均超过95%的运行时间缩减，将重调度时间从数小时缩短至数秒，同时保持了较高的解质量。该两阶段框架在大规模实例上表现优异，在近80%的场景中超越了集中式拍卖，平均解质量提升超过12%。此外，其分别在59%和28%的场景中优于模拟退火算法的平均结果和最佳结果，提供了实时任务连续性所需的鲁棒的速度-质量权衡。