Beyond Manual Planning: Seating Allocation for Large Organizations

TL;DR

This paper presents an automated framework for seating allocation in large organizations, optimizing team placements based on hierarchical proximity to improve over manual planning methods.

Contribution

It introduces the Hierarchical Seating Allocation Problem (HSAP) and a scalable end-to-end solution combining probabilistic road maps, RRT, heuristic search, and integer programming.

Findings

Effective in large-scale instances

Quantitative and qualitative evaluation results

Improves seating arrangement efficiency

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.