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.

翻译：缓冲区在生产线系统中至关重要，用于解耦连续工序。在密集型地面存储环境（如空间受限的旧厂区）中，人工操作正面临严重的劳动力短缺和运营成本上升挑战。此类区域的自动化需解决缓冲区存储、检索与重排问题（BSRRP）。虽然已有研究针对仅涉及重排和检索固定物品集的场景，但实际制造业需采用自适应方法来处理不断到达的单元负载。本文提出多AMR BSRRP，协调机器人车队在共享地面区域中管理并发重排及带时间窗的存储与检索任务。我们构建了二元整数规划（IP）模型，以获取用于基准测试的精确解。由于该问题属于NP难问题，精确方法在工业规模下计算不可行，我们提出一种分层启发式方法。该方法将问题分解为：基于A*搜索的单元负载任务级序列规划，以及基于约束规划（CP）的多机器人协调与调度。实验表明，与精确模型相比，计算时间减少了数个数量级。这些结果证实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。