Anton Ipsen, Michael Cashmore, Parisa Zehtabi, Nicolas Marchesotti, Kirsty Fielding, Daniele Magazzeni, Manuela Veloso

Hierarchical Seating Allocation (Extended Abstract)

Introduction

Hierarchical seating allocation (extended abstract). Optimize hierarchical seating for large organizations with HSAP. Our framework automates team proximity allocation using PRM, RRT, heuristic search & integer programming, replacing manual planning.

Abstract

The Hierarchical Seating Allocation Problem (HSAP) is the problem to allocate an organizational hierarchy of teams to a set of seats 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.

Review

This extended abstract introduces the Hierarchical Seating Allocation Problem (HSAP), a pertinent challenge for large organizations aiming to optimize workspace utilization based on team hierarchy. The authors compellingly articulate the need for an automated solution, highlighting the current inefficiencies of manual processes which lead to infrequent and suboptimal seating arrangements. The core motivation stems from the necessity to ensure teams with close hierarchical ties are seated in close proximity, such as within contiguous areas, a crucial factor for fostering collaboration and operational efficiency. The proposed end-to-end framework directly addresses this significant practical gap. The methodology presented for tackling the HSAP is multi-faceted and technically sophisticated. A key innovation lies in the scalable approach to calculating distances between any pair of seats, utilizing a combination of Probabilistic Road Maps (PRM) and Rapidly-exploring Random Trees (RRT). This foundation for accurate and efficient distance metrics is then integrated into a comprehensive solver that employs heuristic search, dynamic programming, and integer programming. This hybrid approach suggests a robust strategy to navigate the combinatorial complexity of the HSAP, aiming to balance computational tractability with solution quality. The work promises significant practical implications by offering a systematic alternative to current manual methods, potentially leading to more efficient, adaptable, and optimal seating allocations. The authors indicate that their approach has been demonstrated under various instance sizes, with both quantitative and qualitative evaluations of the PRM framework and subsequent allocations. This commitment to demonstrating effectiveness across different scales strengthens the credibility of the proposed framework. Overall, this extended abstract outlines a promising and well-conceived approach to a real-world problem, showcasing a blend of algorithmic innovation and practical utility.

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