arXiv:2603.26542cs.ROcs.AI2026-03

多AGV协同调度,解决高密度工厂中存取与重排的实时难题

The Multi-AMR Buffer Storage, Retrieval, and Reshuffling Problem: Exact and Heuristic Approaches

  • 分层启发式算法:先规划任务序列,再协调多机器人
  • 相比精确模型,计算时间减少数个数量级
  • 适合高密度工厂的实时动态调度场景

缓冲区在生产系统中用于解耦连续工序。在空间受限的老旧工厂等密集存储环境中,人工操作因劳动力短缺和运营成本上升而愈发困难。自动化缓冲区需解决缓冲存储、取货与重排问题(BSRRP)。以往研究多聚焦于固定物品的重排与取货,但实际制造需求还需应对到达的物料单元。本文提出多AGV-BSRRP,通过机器人集群在共享地面区域中协同完成并发重排、带时间窗的存取任务。我们构建了二元整数规划(IP)模型以获得基准精确解。由于该问题为NP-hard,精确方法在工业规模下不可行,因此提出一种分层启发式算法:首先使用A*搜索进行物料单元放置的任务级序列规划,再采用约束规划(CP)实现多机器人协调与调度。实验表明,相比精确公式,计算时间显著降低数个数量级,验证了该启发式方法作为高密度生产环境响应式控制逻辑的可行性。

原文摘要 · Abstract (English)

Buffer zones are essential in production systems to decouple sequential processes. In dense floor storage environments, such as space-constrained brownfield facilities, manual operation is increasingly challenged by severe labor shortages and rising operational costs. Automating these zones requires solving the Buffer Storage, Retrieval, and Reshuffling Problem (BSRRP). While previous work has addressed scenarios where the focus is limited to reshuffling and retrieving a fixed set of items, real-world manufacturing necessitates an adaptive approach that also incorporates arriving unit loads. This paper introduces the Multi-AMR BSRRP, coordinating a robot fleet to manage concurrent reshuffling, alongside time-windowed storage and retrieval tasks, within a shared floor area. We formulate a Binary Integer Programming (IP) model to obtain exact solutions for benchmarking purposes. As the problem is NP-hard, rendering exact methods computationally intractable for industrial scales, we propose a hierarchical heuristic. This approach decomposes the problem into an A* search for task-level sequence planning of unit load placements, and a Constraint Programming (CP) approach for multi-robot coordination and scheduling. Experiments demonstrate orders-of-magnitude computation time reductions compared to the exact formulation. These results confirm the heuristic's viability as responsive control logic for high-density production environments.

机器人调度生产优化多智能体运筹优化

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