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

2026-03-27

AI Summary

This study addresses the challenge of coordinating multiple autonomous mobile robots (AMRs) in high-density manufacturing environments to jointly execute storage, retrieval, and reshuffling tasks with time windows. It is the first to incorporate dynamically arriving storage tasks into a buffer job scheduling framework. The authors propose a scalable hierarchical heuristic: an upper layer optimizes the sequence of unit-load tasks using A* search, while a lower layer employs constraint programming for real-time multi-robot coordination. A binary integer programming model is also developed to serve as an exact solution benchmark. Experimental results demonstrate that the proposed approach achieves solution quality comparable to the exact method while reducing computation time by several orders of magnitude, thereby substantially enhancing the feasibility of real-time control in high-density scenarios.

Abstract

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.

Problem

• Multi-AMR
• Buffer Storage
• Retrieval
• Reshuffling
• Time-windowed Tasks

Innovation

• Multi-AMR BSRRP
• Hierarchical Heuristic
• A* Search
• Constraint Programming
• Buffer Storage and Reshuffling