AI Agents 相关度: 6/10

Beyond Manual Planning: Seating Allocation for Large Organizations

Anton Ipsen, Michael Cashmore, Kirsty Fielding, Nicolas Marchesotti, Parisa Zehtabi, Daniele Magazzeni, Manuela Veloso
arXiv: 2602.05875v1 发布: 2026-02-05 更新: 2026-02-05

AI 摘要

提出层级座位分配问题(HSAP),并提出一个端到端框架进行求解,优化大型组织座位分配。

主要贡献

  • 定义了层级座位分配问题(HSAP)
  • 提出了一个端到端的HSAP求解框架
  • 结合PRM/RRT、启发式搜索和动态规划,用整数规划求解HSAP

方法论

使用PRM和RRT计算座位距离,结合启发式搜索和动态规划,利用整数规划求解HSAP。

原文摘要

We introduce the Hierarchical Seating Allocation Problem (HSAP) which addresses the optimal assignment of hierarchically structured organizational teams to physical seating arrangements on a floor plan. This problem is driven by the necessity for large organizations with large hierarchies to ensure that teams with close hierarchical relationships are seated in proximity to one another, such as ensuring a research group occupies a contiguous area. Currently, this problem is managed manually leading to infrequent and suboptimal replanning efforts. To alleviate this manual process, we propose an end-to-end framework to solve the HSAP. A scalable approach to calculate the distance between any pair of seats using a probabilistic road map (PRM) and rapidly-exploring random trees (RRT) which is combined with heuristic search and dynamic programming approach to solve the HSAP using integer programming. We demonstrate our approach under different sized instances by evaluating the PRM framework and subsequent allocations both quantitatively and qualitatively.

标签

优化 座位分配 组织管理 整数规划

arXiv 分类

cs.AI math.OC