自动优化大型组织的团队座位分配，让上下级就近办公。
Beyond Manual Planning: Seating Allocation for Large Organizations
- 用概率路图和快速探索树计算座位间距离，提升效率。
- 结合启发式搜索与整数规划，实现层级团队的最优布局。
- 适合需要频繁调整办公布局的大企业或研究机构。
我们提出层级座位分配问题（HSAP），旨在为具有复杂层级结构的大型组织，优化团队在楼层平面图中的物理座位安排。该问题的核心是确保具有紧密层级关系的团队尽可能相邻而坐，例如使一个科研小组占据连续区域。当前这一过程依赖人工操作，导致重排频率低且结果不优。为此，我们设计了一个端到端框架来解决HSAP：采用基于概率路图（PRM）和快速探索随机树（RRT）的可扩展方法计算任意两个座位间的距离，并结合启发式搜索与动态规划，通过整数规划求解最优分配。我们在不同规模实例上评估了该方法，从定量和定性两方面验证了其有效性。
原文摘要 · Abstract (English)
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.
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