提出简单高效调度策略，显著降低多机器人系统队列延迟。
Exhaustive-Serve-Longest Control for Multi-robot Scheduling Systems
- 当前有任务则全力服务，空闲时跳转至最长队列位置。
- 相比基线方法，平均队列长度减少23%，持有成本更低。
- 适合实时性要求高、环境动态的多机器人协同场景。
我们研究具有随机到达和切换延迟的多机器人、多队列系统的在线任务分配问题。时间以时隙划分；每个位置每时隙最多容纳一个机器人；服务消耗一时隙；位置间切换需一时隙旅行延迟；到达为独立伯努利过程。我们建立折扣成本马尔可夫决策过程，并提出一种简单的实时策略——耗尽式最长优先（Exhaustive-Serve-Longest, ESL）：当当前位置非空时进行完全服务，空闲时切换至最长非空未占用位置。我们证明该策略最优。作为对比，我们调优固定停留周期策略并实现先到先服务策略。在不同服务器与位置比及负载条件下，ESL始终表现出更低的折扣持有成本和更小的均值队列长度，且动作时间中服务占比更高、切换更少。其简洁性与鲁棒性使其成为实时多机器人调度系统的实用默认方案。
原文摘要 · Abstract (English)
We study online task allocation for multi-robot, multi-queue systems with stochastic arrivals and switching delays. Time is slotted; each location can host at most one robot per slot; service consumes one slot; switching between locations incurs a one-slot travel delay; and arrivals are independent Bernoulli processes. We formulate a discounted-cost Markov decision process and propose Exhaustive-Serve-Longest (ESL), a simple real-time policy that serves exhaustively when the current location is nonempty and, when idle, switches to a longest unoccupied nonempty location, and we prove the optimality of this policy. As baselines, we tune a fixed-dwell cyclic policy via a discrete-time delay expression and implement a first-come-first-serve policy. Across server-to-location ratios and loads, ESL consistently yields lower discounted holding cost and smaller mean queue lengths, with action-time fractions showing more serving and restrained switching. Its simplicity and robustness make ESL a practical default for real-time multi-robot scheduling systems.
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