We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.

翻译：本文提出块重排问题（BRaP），作为大型仓储管理中涉及在密集网格内重新排列存储块以实现目标状态的关键挑战环节。我们将BRaP形式化定义为图搜索问题，基于滑块拼图问题的启发思路，提出了五种基于搜索的求解算法，这些算法融合了联合构型空间搜索、经典规划、多智能体路径规划以及专家启发式策略。通过实证研究，我们从规划质量与可扩展性两个维度对五种方法进行了评估。尽管搜索空间规模与块数量呈指数关系，我们的方法在80×80网格中为深度埋藏块生成重排规划时仍展现出显著效率。