Automated Allocation of Detention Rooms Based on Inverse Graph Partitioning

TL;DR

This paper introduces an automated system for detention room allocation using inverse graph partitioning, improving efficiency and safety over manual methods.

Contribution

It formalizes detention room allocation as inverse graph partitioning and proposes two heuristic algorithms for optimized solutions.

Findings

Algorithms outperform manual allocation in experiments.

System demonstrates practical application value.

Significant improvements in allocation efficiency.

Abstract

Room allocation is a challenging task in detention centers since lots of related people need to be held separately with limited rooms. It is extremely difficult and risky to allocate rooms manually, especially for organized crime groups with close connections. To tackle this problem, we develop an intelligent room allocation system for detention centers to provide optimized room allocation schemes automatically. We first formalize the detention room allocation problem as inverse graph partitioning, which can measure the quality of room allocation schemes. Then, we propose two heuristic algorithms to achieve the global optimization and local optimization of detention room allocation. Experiment results on real-world datasets show that the proposed algorithms significantly outperform manual allocation and suggest that the system is of great practical application value.