This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theory, aims at finding minimal user ride time schedules in polynomial time. The algorithm can either return optimal feasible routes or it can return incorrect infeasibility declarations, on which feasibility can be recovered through a specifically-designed heuristic. The algorithm is furthermore supplemented by a battery management algorithm that can be used to determine charging decisions for electric and autonomous vehicle fleets. Timing solutions from the proposed scheduling algorithm are obtained on millions of routes extracted from DARP and e-ADARP benchmark instances. They are compared to those obtained from a linear program, as well as to popular scheduling procedures from the DARP literature. Results show that the proposed procedure outperforms state-of-the-art scheduling algorithms, both in terms of compute-efficiency and solution quality.

翻译：本文提出了一种新算法，用于高效优化拨号乘车问题（DARPs）中的调度决策，包括考虑电动和自动驾驶车辆的问题变体（e-ADARPs）。该调度启发式算法基于线性规划理论，旨在以多项式时间找到最小用户乘车时间调度方案。该算法能够返回最优可行路线，也可能返回错误的不可行性声明，此时可通过专门设计的启发式方法恢复可行性。此外，算法辅以电池管理算法，可用于确定电动和自动驾驶车队的充电决策。从DARP和e-ADARP基准实例中提取的数百万条路线中，获得了所提出调度算法的时序解决方案，并与线性规划以及DARP文献中流行的调度程序所得结果进行了比较。结果表明，所提出的方法在计算效率和解决方案质量方面均优于最先进的调度算法。