Beyond Manual Planning: Seating Allocation for Large Organizations

2026-02-05

AI Summary

This study formally introduces the Hierarchical Seating Assignment Problem (HSAP), which seeks to optimize physical seating layouts for hierarchically structured teams within large organizations by placing closely related members in spatial proximity. To address this, the authors propose an end-to-end optimization framework that integrates Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT) to model spatial accessibility, combines heuristic search with dynamic programming to derive an efficient distance metric, and employs integer programming to compute a globally optimal assignment. Extensive experiments across scenarios of varying scales demonstrate the method’s effectiveness, with both quantitative and qualitative results significantly outperforming conventional manual arrangements. The approach delivers a scalable and fully automated solution to hierarchical seating allocation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Hierarchical Seating Allocation
• Seat Assignment
• Organizational Hierarchy
• Spatial Proximity
• Floor Plan

Innovation

• Hierarchical Seating Allocation Problem
• Probabilistic Road Map
• Rapidly-exploring Random Trees
• Integer Programming
• End-to-end Optimization Framework